SPE 144326

Evaluation of Production Log Data from Horizontal Wells Drilled in Organic Shales Camron Miller, George Waters and Erik Rylander, Schlumberger

Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE North American Unconventional Gas Conference and Exhibition held in The Woodlands, Texas, USA, 14–16 June 2011. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Production logs from more than 100 horizontal shale wells in multiple basins have been acquired and interpreted. An evaluation of this data set confirms that production is highly variable along the length of the wellbores. In some basins, two-thirds of gas production is coming from only one third of the perforation clusters. Furthermore, when looking at all basins, almost one third of all perforation clusters are not contributing to production. This highlights a significant opportunity to improve overall completion effectiveness and economics in these high profile projects. Observations of near-wellbore reservoir quality and completion efficiency can be attained from the analysis of this data. Rock properties such as mineralogy, natural fracture density, and closure stress in the near wellbore region impact reservoir quality and hydraulic fracture conductivity. Completion parameters such as the staging of stimulation treatments, the number of perforation clusters per frac stage, and perforation cluster spacing can all impact the productivity of an individual perforation cluster. Correlations between productivity and key geologic, petrophysical and completion parameters can be made. The result is a better understanding of the parameters that are controlling completion effectiveness, and corresponding productivity in horizontal organic shale wells. Introduction Operators have been drilling horizontal wells within organic shale for a number of years, with favorable economics. However, not all of these projects have been a complete success as some wells are failing to meet performance expectations. When considering the heightened risk associated with the exploration and development of unconventional gas, success rates are being watched closely and highly scrutinized. This paper reviews a recent evaluation of over 100 production logs collected within horizontal gas shale wells and attempts to explain the variability in production in terms of Reservoir Quality (RQ), Completion Quality (CQ) and Operational Efficiency (OE). Reservoir Quality is defined by those petrophysical parameters of organic shales that make them viable candidates for development. The key petrophysical parameters are: organic content, thermal maturation, effective porosity, fluid saturations, pore pressure, and Gas-In-Place. Completion Quality is defined by those geomechanical parameters that are required to effectively stimulate organic shales. The key geomechanical parameters are: near-wellbore and far-field stresses, mineralogy, specifically clay content and type, and the presence, orientation, and nature of natural fractures. In the context of this work Operational Efficiency is defined as the completion techniques that improve the connection between the reservoir and the wellbore. This study focuses primarily on OE parameters such as perforation cluster number, length and spacing, and fracture stage number and spacing. Where data is available, RQ and CQ are evaluated to aid in the interpretation of the impact of OE parameters on production. The goal is to better understand the parameters that are controlling completion effectiveness, and corresponding productivity in horizontal organic shale wells. Today, most laterals are drilled based on the evaluation of 3D seismic and extensive log data sets collected within offset vertical wellbores, or pilot holes. In most cases the well is steered using a logging while drilling (LWD) gamma ray measurement which can identify significant structural changes, and in many cases vertical variations in bedding. Mud logs are also used to determine mineralogy and identify gas shows. Production data has indicated that lateral placement has significant impact on well performance (Miller et al, 2010). While 3D seismic, offset well logs, LWD gamma ray logs and mud logs all have application, they do not address the small scale vertical variability that exists within shale reservoirs. To optimize productivity, reservoir heterogeneity must be accounted for either during drilling or stimulation.

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Analysis of a large number of production logs acquired along horizontal wellbores in six U.S. gas shale basins suggests that some stimulation stages are underperforming. Figures 1a and 1b portray production log results from two horizontal Arkoma Basin Woodford Shale wells. In Figure 1a, only approximately 50% of the perforation clusters are contributing to the overall production. Figure 1b illustrates an ideal scenario where all perforation clusters are contributing, a phenomenon that only occurs in one out of every five horizontal shale wells. The authors are suggesting that well placement and stimulation with respect to RQ and CQ will result in more uniform production across perforation stages and overall better well performance. Rocks having superior RQ and CQ should be targeted as they will impart better drilling and completion efficiency. Lateral Heterogeneity Heterogeneity in lateral wellbores is primarily controlled by wellbore geometry and vertical variations in rock characteristics, which occur at an extremely small scale in shale reservoirs. Small scale lateral variability in reservoir properties related to digenetic processes have been noted, but the impact on well performance is not yet clear. In general, rock properties at a log scale change slowly in a lateral direction. An exception to this would be natural fracture density, which can change rapidly. Larger scale lateral variability is primarily controlled by shale depositional processes (Bohacs, 2009). Rock properties and natural fracture distribution within shales have significant implications to horizontal stimulation. Strong relationships between natural fractures, minimum horizontal stress (σh) and mineralogy have been observed (Miller, et al, 2010) Natural and drilling induced fracture distribution correlates well to the intrinsic properties of the rock in the absence of local structure (Figs. 2a and 2b). The two outliers on the plot of fracture density versus volume of clay represent zones where carbonate content increases (Fig. 2b). If local structure is influencing the stress state in the rock, it may drive natural fracture distribution as well (Rich and Ammerman, 2010). Figures 3a, 3b and 3c show an example where local structure is controlling fracture distribution in a U.S. shale play. Note how the dip magnitude of the natural fracture is related to that of bed boundaries along the horizontal section, indicating the age of the fractures with respect to structural deformation. Typically, there will be a notable increase in natural fracture density along major structural features. In this example, where a fold and fault complex was encountered during drilling, natural fracture density remains high throughout the entire horizontal section. These data can be integrated and used to subdivide the reservoir based on lateral heterogeneity and should guide stage organization and perforation placement. Vertical and lateral variability must be addressed, preferably during drilling, in order to increase the potential for an economic success. Doing so has shown to positively impact shale productivity (Baihly, et al, 2010). Horizontal Production Logging Production logging is often used in shale reservoirs to evaluate the success of horizontal well placement and reservoir stimulation. The FloScan Imager* (FSI) is widely used in the industry for production logging in multiphase horizontal wells. The tool provides a phase area measurement (holdup) and phase velocity measurement of gas, oil and water in the wellbore using advanced probe and micro-spinner technology. The end product is a direct, down-hole calculation of multi-phase production. This technology has been applied successfully on numerous wells in most U.S. shale plays. When compared to seismic, micro-seismic, petrophysical, geological, completion and stimulation data, it becomes an invaluable tool for realizing what is required for optimal well performance (Waters, et al, 2006, Bazan, et al, 2010, Inamdar, et al, 2010). The FSI was developed to evaluate highly deviated and horizontal wellbores. The tool has a retractable arm which provides a caliper measurement and contains five miniature spinners designed to measure the well fluid velocity profile and a series of electrical and optical probes for measuring localized water and gas holdup (Fig. 4a). Another spinner and probe combination was placed on the main body of the tool in order to measure flow properties on the bottom side of the wellbore. The tool sits on the bottom side of the wellbore, with the retractable arm extending to the top side. All measurements are made at the same depth simultaneously. Six water holdup and six gas holdup measurements identify the phase/fluid type and the five miniature spinners make a direct, localized measurement of the velocity of the fluid passing through it, enabling calculation of a multiphase velocity profile. The unique design of the FSI allows it to measure velocity variations that cannot be detected using conventional production logging tools. The series of micro-spinners provide measurements of mixed and segregated flow regimes, including a direct independent measurement of gas velocity in a multiphase horizontal well (Fig. 4b). Detection of water recirculation down-hole can also be achieved with this tool. The tool is combinable with other cased-hole logging tools and can be deployed using coiled tubing, wireline or tractor systems. Horizontal Production Log Data Set Most of the production logs used for this study are FSI datasets which provide more accurate flow measurements than possible with conventional production logging tools in horizontal wells and unambiguous flow profiling regardless of phase mixing or recirculation. There are a limited number of PS Platform* (PSP) logs included in the dataset. These wells were included only when log quality data was high, essentially, when water volume is low, in order to improve the statistical significance of the dataset.

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No effort has been made to compare the time at which the production logs were run. Production results may also be impacted by varying wellhead flowing pressures among the wells at the time of logging. The impact of water production on the production log results was not assessed, yet most wells were producing little fluid at the time of logging. Wells with very high water production were eliminated from this dataset. These wells are either producing fracturing fluid shortly after the stimulation treatments, or extraneous formation water. This is inferred to be fracturing fluid in most cases, although some wells were eliminated because they indeed are producing water from surrounding formations. Other wells were eliminated due to the inability of the logging tool to reach the furthest perforations. There are six basins represented within the dataset under review in this paper. The basins and well count are shown in Table 1. The Woodford Shale and Barnett Shale make up the majority of the wells in the dataset. For this reason, basin specific correlations are constrained to these two basins in most cases as the small number of horizontal production logs in the other basins does not provide statistical significance. Production Normalization To respect the proprietary nature of the actual production from the wells in the dataset all production data was normalized by basin. The best producing well was assigned a normalized rate of 1.0. The flow rate from lesser producing wells is shown as a fraction relative to the best producing well. Therefore, for figures shown in this paper in which all six basins are displayed, there will be six wells showing a normalized rate of one. This was done so that wells from the Haynesville Shale did not skew the dataset, as the Haynesville Shale wells all produce at the high end of the complete well dataset. Where appropriate, figures are shown for given basins to demonstrate a particular point being made. This allows basin specific trends to be seen that may not be clearly visible when assessing the complete dataset. Wellbore Trajectory: Azimuth Horizontal wells in organic shales are commonly drilled in the direction of σh in order to create closely spaced, transverse hydraulic fractures. For thick intervals with extremely low permeability this orientation is superior to wells drilled in the direction of maximum horizontal stress (σH) where hydraulic fractures collinear with the wellbore are created (Crosby, Yang, and Rahman, 1998). A lateral orientation aligned with principle horizontal stresses can create development problems for operators when their lease boundaries do not permit long laterals to be drilled in either of these directions. In such cases the operator commonly chooses to drill in the azimuthal direction that yields the longest lateral. This introduces two potential problems:

1. As the azimuth of the lateral deviates from the direction of σh perforation clusters must be placed further apart in order to create the same spacing between hydraulic fractures in the reservoir. The required increase in spacing is equal to 1/Cosine of the angle from σh. For example, if the well is drilled 20 degrees from σh then the perforation cluster spacing should be increased by 6.4%: 1/COS(20) = 1.064. This requires an additional 256 ft of lateral compared to a 4,000 ft drilled in the direction of σh. If the perforation cluster spacing is kept the same then hydraulic fractures will be 6% percent closer for the mis-aligned well. For a 75 ft perforation cluster spacing the resulting fracture spacing will be 70.4 ft for the mis-aligned well. For closely spaced fractures this may be sufficient to cause interference between simultaneously propagating hydraulic fractures.

2. A shear component is introduced at the wellbore when the lateral is not aligned with a principle stress. This shear slip can cause fracture width restrictions resulting in increased pressure losses and difficulty placing proppant during the stimulation treatment. The width of the mis-aligned fracture is equal to the product of the axial fracture width and the Cosine of the angle from σh (Weng, 1993). For example, given an axial fracture width at the wellbore of 0.2 inches and an azimuthal deviation of 20 degrees from σh the width of the fracture propagating away from the wellbore will be 0.187 inches. This width restriction will cause an increase in pressure as the tortuous Net Pressure increase is a function of 1/width3 (Plahn, et al, 1995).

Most wells in the dataset are drilled in a NW-SE to N-S direction (Fig. 5). The azimuth of σh has been well established in the Barnett Shale (Fisher, et al, 2002) to be in a NW-SE azimuth. An evaluation of Barnett Shale wells shows that indeed most lateral azimuths are N140o to N320o (Fig. 6). The wells drilled to the southeast are the best performing wells and all are deviated less than 80 degrees. Lease constraints for many Woodford Shale wells in the Arkoma Basin dictate that wells be drilled in a N-S direction to eliminate the need for short laterals in the SW and NE corners of a section. The azimuth σh in the basin as determined from microseismic monitoring is NNW-SSE (Vulgamore, et al, 2007 and Waters, et al, 2009). Figure 7 shows that most wells in the basin are drilled in a N-S azimuth. Three of the four best producing wells are drilled in a N-S azimuth. On the low productivity end there does appear to be a correlation with poor productivity and wells in a N-S direction. All wells producing in the bottom 20% of the data set are drilled in this direction. The best performing wells not being aligned with σh is contrary to generally accepted beliefs. An explanation may reside in Tables 2 and 3. The Woodford shale has the longest stimulation stage length in the dataset, and the second largest spacing between perforation clusters. Hydraulic fractures in the Arkoma Basin are

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frequently long and relatively narrow (Vulgamore, et al, 2007 and Waters, et al, 2009). This is due to the present day compressional geologic setting in the basin that imposes a higher effective stress on natural fractures oblique to σh. Thus the laterals not aligned with σh will create a more closely spaced fracture system that should improve well productivity. Wellbore Trajectory: Deviation Most initial horizontal shale wells were drilled at deviations greater than 90 degrees to facilitate gravity drainage of fracturing fluid. As the importance of lateral landing point has become accepted (Miller, et al, 2010) the trend has been to drill laterals on structure regardless of the resulting trajectory. A comparison of lateral deviation to lateral productivity (Fig. 8) shows normalized production versus wellbore deviation for all wells in the study. Six of the eight wells in the top 80th percentile are deviated greater than 90 degrees. The notable exception is the well with a normalized rate of 1.0 at a deviation of less than 82 degrees. This is a Barnett Shale well that was completed in four stimulation stages via 4 ½ inch casing. The high gas rate coupled with the small diameter casing is effectively unloading water along this lateral. This is one of the few wells in the study completed with 4 ½ inch casing.

Figure 8 does show an inverse trend between well productivity and deviation. All but one of the wells in the 50th to 80th percentile are drilled at deviations less than 90 degrees. The large majority of the wells producing in the bottom 40th percentile are drilled at deviations of greater than 90 degrees. The trend is not consistent across all basins though. Figures 9 and 10 show well productivity versus deviation for the Woodford Shale and Barnett Shale, respectively. In the Woodford Shale the majority of the wells have been drilled at deviations greater than 90 degrees. The poorest wells are those that are drilled at deviations of less than 90 degrees, with the best well drilled at less than 90 degrees only producing at the 31st percentile. Conversely, in the Barnett Shale the majority of the wells producing above the 40th percentile are drilled at deviations of less than 90 degrees. Flow rates on all of these high end wells are adequate to lift fluid from laterals with 4 ½ inch or 5 ½ inch casing. Lateral azimuths for the best Barnett Shale wells are to the SE (Fig. 6). From this study it is inconclusive whether deviations greater or less than 90 degrees directly impacts production in the Woodford Shale or Barnett Shale. Perhaps deviations of greater or less than 90 degrees will be reservoir specific. Additional production logs are required to clarify this issue. Fracture Staging Analysis Not surprisingly, well productivity improves with an increase in the number of fracture stages. Figure 11 shows that all wells but two producing above the 60th percentile have at least 7 stimulation stages performed on them. The majority of the wells producing in the bottom 20th percentile have 5 stages or fewer. One could argue that the production increase is a function of increasing lateral lengths only. Indeed, it is true that lateral lengths are increasing. The average lateral length in the study is shown by basin in Table 2. But Figure 12 shows that the correlation with productivity is much weaker for lateral length than for the number of fracturing stages. This infers that stimulating shorter sections of the lateral is having a positive impact on productivity. Indeed, this does appear to be the case. Figure 13 shows that there are only three wells with average stimulation stage lengths greater than 500 ft that are producing in the top 50th percentile. The trend is consistent across the basins with one interesting exception (Figs. 14 – 18). Data from the Woodford Shale shows that all wells producing above the 30th percentile have stage lengths less than 490 ft. The best well with a frac stage length greater than 600 ft only produces at the 25th percentile. The Barnett Shale shows a similar trend with only two wells having average stage lengths greater than 410 ft producing above the 40th percentile. The exception is the Eagle Ford Shale which shows improved production with increased fracture stage lengths. But note that the Eagle Ford Shale wells have the shortest lateral lengths covered by a frac stage. Figures 14-18 indicate that for most basins the optimum fracture stage length is in the 300 ft to 400 ft range. Correlations in the Haynesville Shale and Eagle Ford Shale are based on a small dataset and conclusions may change as additional production logs are acquired. This spacing issue will be addressed in more detail in the perforation stage analysis later in this paper. The length of the interval between fracture stages was also analyzed. Many operators utilize greater distances between the heel perforation cluster on one stage and the toe perforation of the subsequent stage, than what is employed between perforation clusters within a stage. The argument is that any fracturing pressure increase associated with fractures in close proximity will self regulate by forcing the perimeter fractures in a stage to propagate a further distance from the adjoining fractures. If this occurs then the heel fracture may grow toward the interval to be stimulated in the next stage, thus justifying a larger distance between fracture stages. The average spacing between fracture stages in this dataset is 168 ft. The average perforation cluster spacing within a fracture stage is 121 ft. So there does appear to be operator buy-in to this theory to some degree. Figure 19 shows this spacing versus normalized rates. A trend of shorter spacing lengths and improved production is indicated. The same trends are evident in the Woodford Shale (Fig. 20) and Fayetteville Shale (Fig. 21). Spacing in the range of 100 ft in the Woodford Shale and less than 100 ft in the Fayetteville Shale result in wells with the best production. This indicates that for the given distances between stages, any stress increase associated with the previous stimulation treatment is not adversely affecting the productivity of the subsequent stages.

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The Barnett Shale (Fig. 22) is the exception to this rule. There is no clear correlation between stage spacing and productivity. A possible explanation is the geologic setting of the Ft. Worth Basin. The basin is situated within a present day extensional environment and natural fractures are most commonly orthogonal to the hydraulic fractures. This means that the horizontal stresses are low and relatively close to each other (Waters, et al, 2006). This setting allows for a wide, complex fracture system to be developed during the stimulation process as is commonly seen in microseismic activity (Fisher, et al, 2002). In such a case, there will be less benefit from closely spaced perforation clusters and fracture stages. Indeed, in this dataset the Barnett Shale had the largest average distance between fracture stages and the largest average perforation cluster spacing at 144 ft. Thus, recognizing two key CQ parameters, stress state and orientation of natural fractures, can aid in determining the optimum fracture stage lengths. Fracture stage production variability was analyzed next. For this analysis the actual stage production was compared to the theoretical stage production, defined as the production rate for a frac stage assuming all stages produce equally. The stages are plotted as a percentage of the stages producing below their theoretical production, with the percentages varying from 10% to 90%. A stage at 10% below the average is within 10% of the theoretical average. Conversely, a stage at 90% below is only producing one tenth of its theoretical average. Figure 23 plots each reservoir’s frac stages that are within 10%, 25%, 50%, 75% and 90% of the theoretical average. From 39% to 49% of the stages are producing within 10% of their theoretical average for all basins. The percentage of stages producing ½ of their theoretical production ranges from 14.6% (Marcellus Shale) to 33% (Haynesville Shale), with an average of 20% for all basins, meaning that one in five stages is producing less than half of its theoretical average. Stages producing at less than 90% of their average range from a low of 0% in the Fayetteville Shale, meaning all stages are producing at some measureable rate, to a high of 8.7% in the Barnett Shale, with an average of 6.5% of all frac stages not producing. Practically this means that for every two wells completed with 8 stage stimulation treatments, there is one stage that is not producing at all. More specifically, this means that multiple perforation clusters are not contributing even though they were theoretically stimulated. This will be discussed in more detail in the Perforation Cluster Analysis section. Specific production variation by stage was then evaluated. This is displayed by basin in Figures 24-29. The stage averages account for variations in the number of stimulation stages for each well within a basin. The early stages will be more statistically meaningful as they will include all or most of the wells in the dataset: all wells have a Stage 1 frac, but not all wells have a Stage 10 frac. Three of the basins, the Woodford Shale, Barnett Shale and Fayetteville Shale, show production decreasing from the toe of the lateral to the heel. The Eagle Ford Shale shows productivity declining from the heel to the toe. The Marcellus Shale and Haynesville Shale show no distinct trends. The deviations for all of the wells but one in these two basins are less than 90 degrees. Although not known for lack of horizontal log data, perhaps these wells were drilled to stay in the optimum landing interval. If so, then productivity along the lateral should be more uniform. The low production from the toe stage on the Marcellus Shale and Haynesville Shale wells with 12 stimulation stages may be misleading. Only one well in each of these basins was completed in 12 stages. Perforation Cluster Analysis Recognizing that all stages are not contributing equally to production, an examination of perforation cluster productivity within frac stages was performed to assess the quantities of perforation clusters not contributing along the lateral. Figure 30 shows the number of perf clusters per frac for the complete dataset. Note that the points do not always equal a whole number as the number of perforation clusters per stage along a lateral is not always the same. There is no clear trend in the data. The best wells have as low as two to as many as six perf clusters per frac stage. Certainly wells with eight clusters per frac are poor performers. But to the other extreme, those wells with a single perf cluster vary from good to poor. Data specific to the Woodford Shale and Barnett Shale are shown in Figures 31 and 32, respectively. The Woodford Shale dataset is dominated by wells completed with four perf clusters per stage. But this reservoir is the source of the poor wells with eight clusters per stage. It is difficult to effectively stimulate this many perforation clusters in a single stage. The Barnett Shale is the source of most of the wells with one perf cluster per stage. There is a trend toward better wells with fewer perforation clusters, with all wells producing above the 55th percentile averaging from 1 to 2.3 clusters per stage. As mentioned in the frac stage discussion, the Barnett Shale is more amenable to fewer perf clusters and frac stages because of the geologic setting that promotes complex fracturing. Another key feature is the high silica and low clay volumes which promote a high Young’s Modulus. The Barnett Shale has low and near equal horizontal stresses, a natural fracture system orthogonal to the hydraulic fractures, and a high average Young’s Modulus. The result is that a larger percentage of a complex fracture system is likely to remain open during production than in other reservoirs evaluated in this study. Production data analysis (Baihly, et al, 2010(2)) has shown that the Barnett Shale has a lower decline rate than all other shale reservoirs to date. These CQ parameters in the Barnett Shale allow wells to be completed with a greater fracture spacing as previously discussed and fewer perforation clusters. Wider cluster spacing is indeed employed in the Barnett Shale (Table 3). These CQ parameters are unique to organic shales under development today and help make the Barnett Shale, The Barnett Shale.

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In addition to the number of perforation clusters within a stimulation treatment, the spacing of the clusters is relevant as well. Perforations that are placed too close to each other can induce fracture interference resulting in higher fracturing pressures and the creation of fewer dominant fractures as some fractures close and others accept more fracturing fluid. Warpinski and Branagan (1989) found that the stress increase in the reservoir at a given distance from a hydraulic fracture is a function of the fracturing Net Pressure and the height of the hydraulic fracture, with greater fracture heights increasing the stress for a given Net Pressure. The stress increase decays with distance from the fracture as a function of half of the fracture height. Based on the work of Warpinski and Branagan (1989), a fracture (perforation cluster) spacing of 75 ft and a gross fracture height of 300 ft will cause σh to increase by approximately 60% of the Net Pressure. If 250 psi of Net Pressure is developed, then the stress at the next perforation cluster 75 ft away will be 150 psi higher. Organic shales have not exhibited a significant stress increase because the Net Pressure build during the stimulation has been low due to the low Young’s Modulus common to argillaceous organic shales, and the low viscosity slickwater fluids that are commonly employed. An analysis of the average perforation cluster spacing for all wells in the study is shown in Figure 33. The spacing ranges from 36 ft to 421 ft. The large majority of the wells have spacing between 75 ft and 175 ft. The trend is that cluster spacing of less than 125 ft tends to produce superior results with all but two wells having this spacing in the wells producing above the 55th percentile. Of interest is the difference in results between the Barnett Shale and the Woodford Shale (Figs. 34 and 35). A close perforation cluster spacing does not seem to be beneficial in the Barnett Shale. There are as many wells with cluster spacings of 175 ft or greater producing above the 40th percentile as there are below it. This is further evidence that a wide, complex fracture system is being created during stimulation of the Barnett Shale. To the contrary, in the Woodford Shale all wells but one that are producing above the 30th percentile have cluster spacings of less than 133 ft. The Barnett Shale has an average perforation cluster spacing of 183 ft while the Woodford Shale’s spacing averages 130 ft. The average spacing for each reservoir is shown in Table 3. The shale reservoirs that have been exploited more recently (Haynesville Shale, Marcellus Shale and Eagle Ford Shale) have utilized the closest cluster spacing. This is perhaps recognition by operators that closely spaced hydraulic fractures are beneficial in rocks with permeabilities in the range of 0.0002 md (Waters, et al, 2009). The productivity of perforation clusters within a stage was evaluated next. Figure 36 shows the percentage of perforation clusters that are not producing for all six basins reviewed in this work. Considering all perforation clusters, those which are producing 110% to 150% of the theoretical nominal production are referred to as better, and clusters that are producing greater than 150% of nominal are best. The figure shows that non-productive perforation clusters range from a low of 21% in the Eagle Ford Shale to a high of 32% in the Woodford Shale. The Marcellus Shale and Woodford Shale show the greatest number of clusters not contributing. These two basins have the greatest spatial variability in CQ of all six basins studied. The Arkoma Basin has undergone significant structural deformation post Woodford Shale deposition. The present day strike-slip, or approaching strike-slip, stress regime results in significant stress variations within the basin. The Marcellus Shale was deposited in a restricted environment (Arthur, 2010) resulting in significant changes in mineralogy across the basin. This mineralogy change has been shown to impact stress (Waters, et al, 2006). There are similar productivity trends for the higher producing stages as well. When considering only the best wells there are still many clusters that are not contributing. This ranges from a low of 6% in the Haynesville Shale up to 22.5% in the Woodford Shale. Weighting the results by the number of wells per basin, the average number of perforation clusters not producing in the best stimulation stages is 19%. For all wells the number is 29.6%. This suggests that current completion methodologies, while operationally very efficient, are not resuting in optimized completions. The amount of perforation clusters not contributing is a significant number considering that limited entry perforating is used on the stimulation treatments. This is either an indication that limited entry is not effective or that there are CQ variations along the lateral, associated with the wellbore penetrating multiple beds within the shale, that impact productivity. It is common to see treatment pressure drops in excess of 2,000 psi during the stimulation. Losing this much perforation and/or near-wellbore friction pressure can result in perforation clusters placed in higher stressed intervals becoming inactive. Most completion designs do not employ +2,000 psi of perforation friction, assuming all holes are open. This may infer that all holes, or more relevantly, all clusters are not active. This could be associated with the differences in fracture initiation pressure commonly encountered along the lateral. The end result is that production within a fracture stage is not optimized. By reducing the number of perforation clusters within a stage one increases the odds of all clusters being stimulated as long as limited entry is employed. Therefore, production log analyses should show improved flow distribution when fewer clusters are utilized in a frac stage. Figure 37 depicts cluster productivity versus the number of clusters per frac stage. Again, results are shown for all frac stages, those stages producing above average, and the best stages as previously defined. The result is a weighted average by basin for all wells in the study. Clearly, as the number of perforation clusters per stage increases so does the number of non-producing perforation clusters. Nearly half of all perforation clusters are not producing when six clusters per frac stage are employed. Even when only two clusters per stage are used 20% of these clusters are not contributing. The

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trend is similar for the better and best producing stages as well. Even in the best stages over 46% of the perforation clusters are not contributing when 6 clusters per stage are employed. While increasing the number of clusters per frac stage improves OE by reducing the number of frac stages and corresponding bridge plugs and perforation runs, perforation cluster productivity is significantly compromised. A limited number of wells in the dataset were perforated using 0-180 degree phasing, with holes oriented to the top and bottom of the wellbore. This is the orientation of the hydraulic fractures on the borehole wall when overburden is the greatest principle stress (El Rabaa, 1989). The intent is to perforate directly into the fracture plane at the borehole. The majority of the clusters are oriented with a 60 degree phasing utilizing 6 shots per foot. Figure 38 shows that the wells employing 0-180 degree phasing, all fall in the bottom 35th percentile of productivity. While this limited dataset is not adequate to make definitive conclusions, it does appear that this phasing is not ideal. Excentered weighted subs are used to align the perforations to the top and bottom of the hole. Perhaps these subs are not always successful at correctly orienting the guns. Horizontal Image Logging Borehole images have significant application in horizontal shale wells, but very few are run due to cost considerations. Well placement in zones with superior RQ and CQ is believed to be crucial for optimum drilling efficiency, reservoir stimulation and gas drainage. Waters et al (2006) discussed how borehole images can be used to optimize the stimulation of horizontal shale wells by tailoring the completion design with respect to lateral changes in reservoir characteristics. Stimulation staging organization, isolation of high stress intervals, perforation cluster placement and proppant scheduling decisions can be made after the analysis of borehole images. This process should increase the potential for maximum reservoir contact during and production after the stimulation. Borehole images of all types (resistivity, acoustic, density, etc.), can identify bed boundaries, which allow one to determine the location of the wellbore within the stratigraphic section, either while drilling or post drill. Figure 39 illustrates how bedding planes will appear on image logs when traveling up or down-section. This information is not only useful in geosteering and understanding well placement, but also in constructing a near-wellbore 3D structural model, or update existing field-wide maps. Bed deformation or curvature occurring as a result of nearby faulting or folding can be readily identified on borehole images, used to estimate local stresses, and considered in both the completion plan and reservoir modeling. Subseismic structures are perhaps the most common source of problems encountered when trying to drill a target zone. These features can be identified and accounted for in order to avoid or minimize drilling out of zone. Borehole micro-resistivity images are an excellent tool for evaluating both natural and drilling induced fractures. The existence of natural fractures is believed by many to be critical to the economic success of shale plays. Natural fractures increase system permeability within these ultra-low permeability rocks and can promote hydraulic fracture complexity during stimulation (Fig. 40). As stated, natural fractures enhance CQ within the Barnett Shale (Waters, et al, 2006). Although most natural fractures within organic shale are thought to be healed, they are often reopened either during drilling or stimulation and enhance both hydraulic fracture complexity and the connection between the reservoir and wellbore. Understanding the natural fracture networks and trends within any reservoir provides an advantage when planning and executing a strategic field development. Drilling induced fractures and borehole breakout can be identified using borehole images. Drilling induced fractures are more common and provide insight into the state of stress along horizontal wellbores. These fractures are small scale hydraulic fractures and provide an indication of near-wellbore stress magnitude and variation. Zones with a high frequency of drilling induced fractures are likely to be locations with lower fracture initiation pressures. Two types of drilling induced fractures exist within horizontal wellbores, longitudinal and transverse, both initiating at the top and bottom of the borehole in extensional environments where overburden is the principle stress (Fig. 41). When drilling induced fractures do not exist, an overall high stress environment is likely and higher stimulation pressures should be expected. Waters et al (2006) explains that the nature of these fractures can provide valuable information which can be used to enhance the completion plan. Borehole micro-resistivity images respond to mineralogy. Resistive minerals, such as silica and calcite, appear light colored on the image log, while conductive minerals, such as clay minerals and pyrite, appear dark in color. This qualitative indication of mineralogy is very useful when landing and stimulating lateral shale wellbores. Low clay intervals are the targets in most shale plays due to their superior RQ and CQ parameters. These intervals typically contain more gas, are easier to drill and can be stimulated more easily. In silica-rich shales, such as the Barnett Shale, Fayetteville Shale and Woodford Shale, where carbonate content is relatively low, stress is inversely proportional to the resistivity of an interval when no tectonics are present (Waters, et al, 2006). In addition, the resistivity of the interval is directly related to clay volume (Waters, et al, 2006, and Miller, et al, 2010). These relationships become more complicated in shales where carbonate content is higher, such as the Haynesville Shale and Eagle Ford Shale. In these shale reservoirs resistivity is typically highest in the carbonate intervals and the less resistive siliceous zones will appear darker in color on resistivity images. Interpreters must be careful not to condemn these zones, which typically have desirable RQ and CQ. Other tools, which quantify mineralogy, should be employed in these cases. This paper reviews two wells which have borehole micro-resistivity images and a production log along the lateral. For

8 SPE 144326

these wells, production from each stimulation stage and perforation cluster was evaluated and compared to horizontal image logs in order to define relationships between production, RQ and CQ. Geology Case Study 1 The first example is from the Barnett Shale. A GeoVISION* resistivity (GVR), logging-while-drilling image was collected within this wellbore and provided qualitative mineralogy, structural dip definition, natural and drilling induced fracture detection and fault identification. This information was used to guide the stimulation of this infill well. Figure 42 summarizes the borehole image analysis and shows how reservoir characteristics change along the horizontal section due to the well cutting across layers of different RQ and CQ. Waters et al (2006) revealed that clay-rich intervals are likely to exhibit mechanical property and stress anisotropy. Consequently, hydraulic fracture initiation and proppant placement becomes more difficult. These intervals typically have poor reservoir characteristics when compared to low-clay intervals. This is one of the reasons why low-clay intervals are the preferred targets in most shale plays. Figure 43 shows a stage by stage comparison of near-wellbore formation resistivity and hydraulic fracture initiation pressure, which clearly illustrates a relationship between the two parameters. The GVR images in Stage 1 appear dark in color, which suggests a more clay-rich mineralogy. The conductive nature of clay-rich intervals is a function of the bound water associated with the clays in the rock. As discussed, these zones are more difficult to stimulate due to higher in-situ stresses and are known to have hydraulic fracture conductivity issues due to their low Young’s Modulus, and propensity for incompatibility with water-based fracturing fluids. Overflushing stimulation treatments in theses zones, as is common in shale stimulation, can result in an unpropped near-wellbore fracture that hinders production. There were no natural fractures and limited transverse drilling-induced fractures observed within this interval, suggesting high stress anisotropy in the near-wellbore region. As expected, this zone experienced the highest fracture initiation pressure (Fig. 43). In fact, this interval required three separate stimulation treatments at different perforation locations before the interval was successfully stimulated. Even though the GVR images indicated less than favorable RQ and CQ parameters within this interval, the operator considered the entire shale section as pay and chose to stimulate this interval even though three attempts were required, with high associated costs. Such difficulties can be predicted and avoided using borehole micro-resistivity images while drilling or post-drill. Although successfully stimulated, this interval is believed to have only contributed minimally to the overall production of the well. This belief is based on the results from comparable stages in both Barnett Shale and other shale reservoirs, but cannot be confirmed since the production logging tool failed to reach the toe of the lateral. Consequently, production from Stages 1 and 2 could not be distinguished by the production log, but the stage combination did yield 60% of the total well production. Waters et al (2006) suggested that, due to the high stress in the Stage 1 area, the hydraulic fracture created during Stage 1 may have grown laterally into the lower stress Stage 2 interval which may have contributed to the enhanced production from this zone. Stages 2, 3 and 4 were placed in better RQ and CQ rocks, having more resistive mineralogy. The treating pressures observed during the stimulation were lower than those experienced in Stage 1 (Fig. 43). Stage 2 is a low-clay, high silica zone which typically correlates to good RQ and CQ in the Barnett Shale. Drilling induced fractures, both longitudinal and transverse, exist and suggest overall low and isotropic horizontal stresses, which promotes efficient stimulation and complex hydraulic fracture geometry. The transverse drilling fractures, which form parallel to the current σH, are oriented NE-SW (Fig. 42). A small number of partially healed natural fractures were observed in and around this interval as well, having an E-W strike orientation. These natural fractures may have been completely cemented and opened while drilling the lateral. Theoretically, one would expect a wide fracture fairway within this interval due to the isotropic horizontal stress state. In reality, hydraulic fracture monitoring indicated planar fracture geometry. This is likely due to the strike of the natural fractures being almost parallel with the σH azimuth. Much of the stimulation treatment does not appear to have intersected an offset well. Intervals which have yet to be contacted during offset stimulations would contribute more to production than those which have already been stimulated and drained. This may explain the high production of the combined Stages 1 and 2. The Stage 2 interval likely yielded a greater part of the 60% of total well production from this stage. Figure 44 shows the production log results along the length of the lateral. Stage 3 was also placed in low clay, more siliceous shale with favorable RQ and CQ parameters. This zone contained both open and partially healed natural fractures, oriented NNE-SSW and WNW-ESE respectively (Fig. 42). Longitudinal and transverse drilling induced fractures were also present throughout this interval, which again indicates a NE-SW σH direction and the good CQ parameters typical of the Barnett Shale. Stage 3 broke down easily during the stimulation, but yielded only 16.7% of the overall production (Figs. 43 and 44). This value is lower than expected if you consider equal production from each stage, or 25% as nominal. However, hydraulic fracture monitoring data from offset wells suggest that the treatment on this zone propagated into an interval that had previously been stimulated (Waters, et al, 2006). This depleted interval will have a lower stress and a pre-existing fracture system. This pressure sink will reduce the likelihood of complex fracturing.

SPE 144326 9

Stage 4 was situated in similar rock to that observed in Stages 2 and 3 and is not believed to be as significantly influenced by the stimulation of offset wells. This zone has good RQ and CQ, and the resulting production was 23.3% of the overall well production (Fig. 44). This zone broke down easily during the stimulation (Fig. 43). A complex hydraulic fracture fairway was indicated from micro-seismic monitoring. The absence of offset wells in this section likely allowed for a complex fracture to be formed as the stresses in this area have not been altered by production. The fact that this well is an infill well skewed the results a bit from what is commonly observed in shale exploration and development wells, especially in the Barnett Shale, but it adds to the continuous learning curve regarding horizontal drilling within, and hydrocarbon production from, organic shales. Geology Case Study 2 This example is from another U.S. gas producing shale where an FMI*, formation micro-resistivity imaging tool, was run in the horizontal section and where RQ and CQ parameters were changing laterally as the well cut across bedding and subseismic faults. No borehole image data is available for display in this paper, but Figure 45 shows the relationship found between shallow formation resistivity and hydraulic fracture initiation pressure, which is the same as the one observed in the Barnett Shale example. Normalized production is also included in Figure 45, where the best producing stage is assigned a value of 1.0 and production from remaining stages is presented as a fraction of this value. This well is not an infill well and reflects more theoretical relationships as it is not impacted by the stimulation of offset wells. The first 2 stages were placed in clay-rich rock, with poor RQ and CQ parameters. These stages experienced higher treating pressures and yielded only a small fraction of the production when compared to the rest of the stages. In fact, the combined production from these two stages failed to meet the nominal production for a single stage with this completion design. Stages 3, 4 and 5 were situated within low clay rock having good RQ and CQ parameters. These intervals each contributed greater than nominal production. This well, and the Barnett Shale example, both exhibit the benefits associated with landing horizontal shale wells within high RQ and CQ intervals and minimizing exposure to poor quality rock. Petrophysics Case Study Porosity and other RQ parameters from two horizontal shale wells were analyzed and compared to production log results in the same wellbore. Successful correlations between effective porosity and production were established and are shown in Figures 46a and 46b. These graphs plot effective porosity against the percent flow from logged sections. This is used to account for the production logging tool failing to log the entire stimulated interval. Each point represents the average effective porosity over a range that is equal to the perforation cluster length, plus 10 feet on either side of the cluster. In most cases as effective porosity increases the volume of clay decreases, especially smectitic clays. The absence of expandable clays dramatically improves RQ as it generally results in a higher matrix permeability. A higher permeability will directly impact well productivity. Two wells do not make a trend, but unfortunately limited RQ data is available on wells with production logs. These two wells are from different shale basins though. So the correlation is not isolated to only a single basin. Conclusions The best Barnett Shale wells have deviations of less than 90 degrees. The opposite trend occurs for the Woodford Shale. It is inconclusive why the results vary, but good Barnett Shale production from wells deviated less than 90% demonstrates that fluids can be unloaded from laterals that are less than horizontal. The dataset size was insufficient to determine whether deviations greater or less than 90 degrees is beneficial to production in a specific reservoir. The best wells in the Woodford Shale occur when the lateral is not aligned with σh. The Woodford Shale has a large spacing between perforation clusters and fracture lengths are commonly long and narrow. Thus, the closer fracture spacing achieved in the reservoir for wells misaligned with σh appears to be beneficial to production. Wells completed with fracture stage lengths in the range of 300 ft to 400 ft appear to be optimum. As the likelihood of fracture complexity goes up the stage length can increase accordingly. The Barnett Shale generates the most complexity during stimulation and produces effectively at wider spacing, with the best wells producing from stage spacings of 400 ft and greater. Production within 10% of the theoretical average occurs on as little as 39% of all wells in the Fayetteville Shale and as many as 49% of wells in the Eagle Ford Shale. Twenty percent of stages in all wells are producing less than half of their theoretical average. No production was seen on 6.5% of the stages in all of the wells analyzed. Production from the heel section of the lateral is greatest in the Woodford Shale, Barnett Shale and Fayetteville Shale. The toe section produces the best in the Eagle Ford Shale. This production is not associated with wellbore deviation. Further analysis is required once the horizontal production log dataset has grown sufficiently large. The best perforation cluster spacing within shale reservoirs is between 75 ft and 175 ft. Wider cluster spacing is effective in the Barnett Shale where a wide fracture network is common due to the low horizontal stress anisotropy and natural fracture azimuth oblique to the hydraulic fracture. Larger perforation cluster spacing between stages than within stages is detrimental

10 SPE 144326

to production. This is no apparent stress alteration from stimulation justifying a larger spacing although a larger spacing is observed in the dataset. For all wells in the dataset, 29.6% of all perforation clusters are not producing. The range is from 21% in the Eagle Ford Shale to 32% in the Woodford Shale. Even in the best wells, 19% of all perforation clusters are not producing. This varies from 6% in the Haynesville Shale to 22.5% in the Woodford Shale. The best wells utilize two to six perforation clusters per stage, with fewer clusters per stage the better. The best Barnett Shale wells only have one or two perforation clusters per frac stage. Woodford Shale wells employing 8 clusters per stage significantly under produce their peers with fewer clusters per stage. Wells in which 6 clusters per stage were employed had approximately 50% of the clusters not contributing. Even on the best wells, 46% of the clusters are not appreciably flowing when placing 6 clusters per stage. Twenty percent of all clusters were not contributing when only two clusters per frac stage were used. This is a surprisingly high number and represents a significant opportunity to improve productivity if cost effective ways can be utilized to place laterals in intervals with the best CQ, and minimize near-wellbore fracture conductivity damage due to overflushing. The variability observed in the 70% of perforation clusters which are producing is likely the result of the wellbore cutting across layers of differing RQ and CQ, but could also be the result of issues encountered during the stimulation process, possibly the overflushing of stimulation treatments. Perforations which are still cleaning up will appear as non-productive or producing poorly, but could start contributing with time. Time lapse production logging would identify this issue. Comingling production from multiple perforation clusters can create variability as well. Most variability can be explained by evaluating RQ and CQ parameters in the shale reservoir. Poor CQ is the worst case scenario. Good CQ and poor RQ may work, but is not desirable. Good RQ combined with good CQ, in the absence of geohazards, maximizes the potential for an economic success within organic shale wells. Further work is recommended, once more production logs are run within horizontal shale wells having either borehole images or other sophisticated data sets so that similar comparisons can be made and understood.

*Mark of Schlumberger Acknowledgements The authors wish to acknowledge the work of Casey Chadwick and Brian Dupuis for the interpretation of the production logs, Sergio Jerez-Vera, Jenna Salamah, Karthik Srinivasan and Irewole Olukoya for compiling the database, and Helena Gamero Diaz for her technical insight. The authors also wish to thank Schlumberger for permission to publish this work. References Arthur, M. 2010. Plumbing the Depths in Pennsylvania: A Primer on Marcellus Shale Geology and Technology. The Pennsylvania State University College of Agricultural Sciences Cooperative Extension, Marcellus Shale Educational Webinar Series, October, 2010. Baihly, J., Malpani, R., Edwards, C., Yen Han, S., Kok, J., Tollefsen, E. and Wheeler, W. 2010. Unlocking the Shale Mystery: How Lateral Measurements and Well Placement Impact Completions and Resultant Production. Paper SPE138427 presented at the 2010 SPE Tight Gas Completions Conference, San Antonio, Texas, 2-3 November. Baihly, J., Altman, R., Malpani, R., and Luo, F. 2010. Shale Gas Production Decline Trend Comparison over Time and Basins. Paper SPE135555 presented at the 2010 SPE Annual Technical Conference and Exhibition, Florence, Italy, 19-22 September. Bazan, L.W., Larkin, S.D, Lattibeaudiere, M.G., and Palisch, T.T. 2010. Improving Production in the Eagle Ford Shale with Fracture Modeling, Increased Conductivity and Optimized Stage and Cluster Spacing Along the Horizontal Wellbore. Paper SPE138425 presented at the 2010 SPE Tight Gas Completions Conference, San Antonio, Texas, 2-3 November. Bohacs, K.M., The Devil in the Details: What Controls Vertical and Lateral Variation of Hydrocarbon Source and Shale-Gas Reservoir Potential at Millimeter to Kilometer Scales? Houston Geological Society Bulletin, Volume 52, No. 01, September 2009, pp 17-17. Crosby, D.G., Yang, Z., Rahman, S.S. 1998. Transversely Fractured Horizontal Wells: A Technical Appraisal of Gas Production in Australia. Paper SPE50093 presented at the 1998 Asia Pacific Oil & Gas Conference and Exhibition, Perth, Australia, 12-14, October. El Rabaa,W. 1989. Experimental Study of Hydraulic Fracture Geometry Initiated From Horizontal Wells. Paper SPE19720 presented at the 1989 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 8-11 October. Fisher, M.K., Wright, C.A., Davidson, B.M., Goodwin, A.K., Fielder, E.O., Buckler, W.S., and Steinsberger, N.P. 2002. Integrating Fracture-Mapping Technologies To Improve Stimulations in the Barnett Shale. Paper SPE77441 presented at the 2002 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 September – 2 October.

SPE 144326 11

Inamdar, A., Malpani, R., Atwood, K., Brook, K., Erwemi, A., Ogundare, T., and Purcell, D. 2010. Evaluation of Stimulation Techniques Using Microseismic Mapping in the Eagle Ford Shale. Paper SPE136873 presented at the 2010 SPE Tight Gas Completions Conference, San Antonio, Texas, 2-3 November. Miller, C., Rylander, E., Le Calvez, J. 2010. Detailed Rock Evaluation and Strategic Reservoir Stimulation Planning For Optimal Production in Horizontal Gas Shale Wells. Abstract and poster presented at the 2010 AAPG International Conference and Exhibition, Calgary, AB, Canada, 12-15 September. Plahn, S.V., Nolte, K.G., Thompson, L.G., and Miska, S. 1995. A Quantitative Investigation of the Fracture Pump-In/Flowback Test. Paper SPE30504 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, Texas, 22-25, October. Ramsay, J.G., Folding and Fracturing of Rocks. Book published with permission of McGraw-Hill Book Company, New York, copyright 1967. Rich, J.P., and Ammerman, M. 2010. Unconventional Geophysics for Unconventional Plays. Paper SPE131779 presented at the 2010 SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, 23-25, February. Vulgamore, T., Clawson, T., Pope, C., Wolhort, S., Mayerhofer, M., Machovoe, S. and Waltman, C. 2007. Applying Hydraulic Fracture Diagnostics To Optimize Stimulations in the Woodford Shale. Paper SPE110029 presented at the 2007 SPE Annual Conference and Technical Exhibition, Anaheim, California, 11-14 November. Warpinski, N. and Branagan, P., “Altered-Stress Fracturing,” Journal of Petroleum Technology, September 1989, pp 990-997. Waters, G., Dean B., Downie, R., Kerrihard, K., Austbo, L. and McPherson, B. 2009. Simultaneous Hydraulic Fracturing of Adjacent Horizontal Wells in the Woodford Shale. Paper SPE119635 presented at the 2009 SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 19-21 January. Waters G., Heinze, J., Jackson, R., and Ketter, A. 2006. Use of Horizontal Well Image Tools To Optimize Barnett Shale Reservoir Exploitation. Paper SPE103202 presented at the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24-27 September. Weng, X. 1993. Fracture Initiation and Propagation From Deviated Wellbores. Paper SPE26597 presented at the 1993 SPE Annual Conference and Technical Exhibition, Houston, Texas, 3-6 October. Tables

Table 1 – Shale basins represented in study and horizontal production log count by basin.

Table 2 – Average lateral length and stage information by basin.

12 SPE 144326

Table 3 – Average perforation cluster information.

Figures a.

b.

Figs 1a and 1b – Production log comparison of two Woodford Shale wells illustrating the typical variability observed in perforation

performance along the length of the lateral (a) versus the more desirable, uniform production performance (b). The red color represents gas production, while blue indicates water. The red tick marks in second track from the bottom in each example represent

perforation cluster locations.

SPE 144326 13

Fig 2a – Full logging suite and evaluation zoned for intervals of similar rock characteristics. The repeating pattern of red, green and

blue color is employed to distinguish the selected zones, A through H, for stimulation treatments.

0

0.5

1

1.5

2

2.5

3

3.5

0 0.1 0.2 0.3

Aver

age F

ract

ure D

ensi

ty

Average Volume of Calcite

Conductive Fractures per Zone

y = ‐2.4693x + 17.586R² = 0.6607

00.51

1.52

2.53

3.5

5.5 6 6.5 7

Aver

age F

ract

ure D

ensi

ty

Average Horizontal Young's Modulus

Conductive Fractures per Zone

B

H

0

0.5

1

1.5

2

2.5

3

3.5

0.2 0.25 0.3

Aver

age F

ract

ure D

ensi

ty

Average Volume of Clay

Conductive Fractures per Zone

F

y = ‐25.721x + 21.757R² = 0.7914

0

0.5

1

1.5

2

2.5

3

3.5

0.7 0.75 0.8 0.85

Aver

age F

ract

ure D

ensi

ty

Avg min Horz Stress

Conductive Fractures per Zone

y = ‐34.465x + 11.371R² = 0.7419

0

0.5

1

1.5

2

2.5

3

3.5

0.22 0.27 0.32

Average Fracture Den

sity

Avg Vertical Poisson's Ratio

Conductive Fractures per Zone

Fig 2b – Correlation of open natural and drilling induced fractures to mechanical properties and lithology.

14 SPE 144326

Figs 3a, 3b and 3c – Series of figures showing how natural fracture distribution can be controlled by local structure. 3a illustrates

natural fracture character with respect to folding (Ramsay, 1967). 3b is a near-wellbore structural model built from borehole image dip data showing how the well was placed within the local structure. 3c is the FMI image and dip data from which the model was built.

SPE 144326 15

Figs 4a and 4b – FSI tool configuration which allows for accurate production logging in highly deviated to horizontal wells with

complex flow regimes.

Fig 5 – Lateral azimuths for all wells. Fig 6 – Lateral azimuths for Barnett Shale wells.

Fig 7 – Lateral azimuths for Woodford Shale wells. Fig 8 – Lateral deviation for all wells.

Fig 9 – Lateral deviation for Woodford Shale wells. Fig 10 – Lateral deviation for Barnett Shale wells.

16 SPE 144326

Fig 11 – Average Fracture Stages per well for all wells. Fig 12 – Average Lateral Length for all wells.

Fig 13 – Average Fracture Stage Length per well for all wells. Fig 14 – Average Stage Length for Woodford Shale wells.

Fig 15 – Average Stage Length for Barnett Shale wells. Fig 16 – Average Stage Length for Fayetteville Shale wells.

Fig 17 – Average Stage Length for Haynesville Shale wells. Fig 18 – Average Stage Length for Eagle Ford Shale wells.

SPE 144326 17

Fig 19 – Average Stage Spacing for all wells. Fig 20 – Average Stage Spacing for Woodford Shale wells.

Fig 21 – Average Stage Spacing for Fayetteville Shale wells. Fig 22 – Average Stage Spacing for Barnett Shale wells.

Fig 23 – Actual stage production versus the theoretical average production. The columns represent stages that are producing below

the theoretical average by the percentages shown.

18 SPE 144326

Fig 24 – Average Woodford Shale Flow Rate per Frac Stage. Fig 25 – Average Barnett Shale Flow Rate per Frac Stage.

Fig 26 – Average Fayetteville Shale Flow Rate per Frac Stage. Fig 27 – Average Eagle Ford Shale Flow Rate per Frac Stage.

Fig 28 – Average Haynesville Shale Flow Rate per Frac Stage. Fig 29 – Average Marcellus Shale Flow Rate per Frac Stage.

Fig 30 – Average Perf Clusters per Frac for all wells. Fig 31 – Average Perf Clusters per Frac in the Woodford Shale.

SPE 144326 19

Fig 32 – Average Perf Clusters per Frac in the Barnett Shale. Fig 33 – Average Perf Cluster Spacing for all wells.

Fig 34 – Average Perf Cluster Spacing in the Barnett Shale. Fig 35 – Average Perf Clusters Spacing in the Woodford Shale.

29.6%

13.5%

9.6%

25.9%

10.8%

6.0%

21.3%

14.1%

7.3%

24.0%

16.2%

14.4%

26.9%

17.8%19.4%

32.2%

23.5%22.5%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Marcellus Haynesville Eagleford Fayetteville Barnett Woodford

All Stages Better Stages Best Stages

Percen

t of P

erforatio

n Clusters not Flowing

Fig 36 – Percentage of all perforation clusters that are not producing. Grey bar includes all frac stages. The green is for stages

producing from 110% to 150% above the average rate. The red bars are for stages producing greater than 150% of the average rate.

20 SPE 144326

All Stages Better Stages Best Stages

Percen

t of P

erforatio

n Clusters not Flowing

Fig 37 – Perforation cluster productivity as a function of clusters utilized per frac stage.

Fig 38 – Perforation phasing for all wells in the dataset.

Fig 39 – Borehole image examples showing how bedding appears when the well is traveling up section (left) and down section (right).

SPE 144326 21

Fig 40 – Horizontal borehole image and micro-seismic example showing how natural fracture orientation can impact hydraulic

fracture complexity. The toe section of the lateral (top left of top right image) created planar fractures as indicated by microseismic activity in an environment where natural fractures are parallel the hydraulic fractures. When they are orthogonal to the hydraulic

fractures, natural fractures may dilate and create a complex fracture network as was seen in the heel section of the lateral.

At an angle to Max Stress complex frac

σH

σh

Parallel to Max Stress planar frac

σH

σh

Horizontal FMI

complex

planar

Top

Bottom

Top

Side

Side

Fig 41 – Horizontal image log example illustrating various fracture types and how they can be distinguished.

22 SPE 144326

Fig 42 – GVR image from the Barnett Shale with snapshots showing changes in reservoir characteristics corresponding to

stimulation stage organization along the lateral wellbore.

Shallow Resistivity(ohm-m)

Fracture Initiation Pressure

(psi/ft)Stage 1 150 0.80Stage 2 350 0.70Stage 3 500 0.67Stage 4 300 0.69

y = ‐0.0004x + 0.832R² = 0.802

00.10.20.30.40.50.60.70.80.9

0 100 200 300 400 500 600Fracture Initiation

 Pressure (psi/ft)

Shallow Resistivity (ohm‐m)

Barnett Shale ExampleFormation Resistivity vs. Fracture Initiation Pressure

Fig 43 – Comparison of shallow formation resistivity to hydraulic fracture initiation pressures, by stage interval, in a horizontal

Barnett Shale well. Modified from Waters, et al (2006)

SPE 144326 23

Fig 44 – GVR image and production log comparison in a horizontal Barnett Shale well.

Horizontal production log

Production log did not reach TD (toe of well)Stage 4                                                                 Stage 3 Stage 2                                       Stage 1

Shallow Resistivity(ohm-m)

Fracture Initiation Pressure

(psi/ft)

Normalized Production

(best producer = 1.0)Stage 1 8 1.2 0.28Stage 2 5 1.1 0.22Stage 3 632 1.0 0.80Stage 4 581 0.9 0.86Stage 5 724 0.9 1.0

y = ‐0.0003x + 1.1504R² = 0.8232

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 100 200 300 400 500 600 700 800

Fracture Initiation

 (psi/ft)

Shallow Formation Resistivity  (ohm‐m)

Geology Case Study #2Formation Resistivity vs. Fracture Initiation Pressure

Fig 45 – Comparison of shallow formation resistivity, hydraulic fracture initiation pressures, and normalized production, by stage

interval, within a U.S. shale play.

y = 2.6681x ‐ 0.0604R² = 0.5028

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

0.000 0.020 0.040 0.060 0.080 0.100

% Produ

ction Logged

Effective Porosity

Example 1% Production Logged vs. Effective Porosity% Flow From Logged Sections vs Effective Porosity

% Fl

ow Fr

om L

ogge

d Sec

tions y = 5.7002x ‐ 0.1405

R² = 0.9223

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

% Produ

ction Logged

Effective Porosity

Example 2% Production Logged vs. Effective Porosity% Flow From Logged Sections vs Effective Porosity

% Fl

ow Fr

om L

ogge

d Sec

tions

a. b.

Figs 46a and 46b – Plots for two wells that show a correlation between percentage of flow from logged sections and effective

porosity.