integrating seismic, csem, and well-log data for reservoir ... · integrating seismic, csem, and...

268 The Leading Edge March 2012

H o n o r a r y L e c t u r e

Surely seismic tells you everything you need to know about the Earth? This is a widely held view and there is no

doubt that seismic methods are extremely powerful, generally applicable, and can be used to generate high-resolution images of structure and stratigraphy which can guide exploration, appraisal and development projects. However, there are some situations where seismic data fail to answer the geophysical question of interest. It is these situations where complementary sources of information must be used to obtain the information required.

Well-log data provide a useful starting point to under-stand the requirement for, and power of, data integration. A variety of measurements are routinely made when a well is logged. The example in Figure 1 shows resistivity, VP, VS, acoustic impedance, density, and Poisson’s ratio. A petrophys-icist analyzing this well will integrate these measurements to provide an interpretation of the lithology and fluid proper-ties. In this example a clean sand reservoir (shown in yellow in the second track) is overlain by a shale layer (grey), and is charged with oil in its upper part.

Looking first at the measurements of seismic properties, the largest variations in the curves occur at lithology bound-aries, for example between the shales and the underlying sand reservoir. In contrast, the effect on the seismic properties of a change in fluid is small. The black curve in the VP, VS, acoustic impedance and Poisson’s ratio plots represents the in-situ measurement; the blue curve represents the equivalent response when the oil in the reservoir is replaced by water. The difference between the curves is small, illustrating the extremely poor sensitivity of seismic properties to changes in the fluid type. That is not to say that it is impossible to extract fluid information from seismic data; sometimes this is an ex-tremely successful strategy. However, in many situations it is difficult or impossible to do so with certainty.

The resistivity measurement, in contrast, shows a high degree of sensitivity to fluid content, but a relatively low sen-sitivity to changes in lithology. In the oil-saturated region of the reservoir, the resistivity is 1–2 two orders of magnitude higher than in the surrounding water-saturated sediments, where the variation in resistivity is small. Thus, the resistivity log provides a key measurement, used by petrophysicists in determining water saturation and fluid properties in a well.

Often the properties of a reservoir are required away from the well bore, and to do this remote sensing methods are re-quired. The lower left plot of Figure 1 shows an example of an acoustic impedance volume derived from inversion of a 3D seismic volume. Areas of low impedance can be correlat-ed with known hydrocarbon accumulations at well locations, and by looking for areas with similar seismic properties other hydrocarbon-saturated zones can perhaps be predicted. How-ever, the seismic method is sensitive to impedance and im-pedance contrasts, which the fluid-substitution study above

Integrating seismic, CSEM, and well-log data for reservoir characterizationLucy MacGreGor, RSI

illustrates are inherently low in sensitivity to saturation effects (red circled areas in Figure 1).

Electromagnetic methods allow remote measurement of resistivity which is extremely sensitive to the properties and distribution of fluids in a structure. The lower right panel in Figure 1 shows a resistivity section derived from inversion of marine CSEM data acquired over a gas reservoir (shown in red). In the same way that multiple data types are used in an integrated interpretation of well-log data, such resistivity information from electromagnetic surveys is complementary to seismic data, and can improve the constraint on fluid prop-erties when used in an integrated geophysical interpretation.

Of course no method is perfect. There are potential pitfalls in each when applied to resolve a given geophysical require-ment. In the case of CSEM data analysis, it is an obvious (but surprisingly often forgotten) fact that the method measures resistivity and not hydrocarbons directly. Whereas zones of high resistivity can be caused by the presence of hydrocarbon fluids, there are many other causes, such as the presence of cemented sandstone, limestones, volcanics or coals. An in-terpretation of a CSEM data set to provide a profile, section, or volume of the Earth (itself a nonunique process) must be followed by an equally careful interpretation of the resulting resistivity data in terms of rock and fluid properties.

The same is true of seismic data that provide measure-ments of reflectivity, or impedance (or a host of derived at-tributes and properties). These seismic properties must also

Figure 1. (top) Example well-log suite. Tracks show (left to right) water saturation, lithology, resistivity, VP , VS, acoustic impedance, and Poisson’s ratio. (lower left) Impedance volume derived from inversion of a 3D seismic volume. (lower right) Resistivity section derived from inversion of marine CSEM data.

March 2012 The Leading Edge 269

H o n o r a r y L e c t u r eH o n o r a r y L e c t u r e

be carefully interpreted in terms of the underlying rock and fluid properties that give rise to them, and once again this interpretation has uncertainty associated with it. For example AVO and amplitude anomalies may be caused by lithology or thin-bed effects rather than fluids, and saturation is often dif-ficult to interpret on the basis of seismic data alone.

For any given geophysical question, the most robust an-swer will be obtained by using the tool, or combination of tools best suited to the task, and integrating the resulting data within a rock physics framework, to provide a shared Earth model that is geologically reasonable, and consistent with each of the geophysical data types available. This idea will be illustrated first using a synthetic case study, before moving on to a real example from the North Sea.

Synthetic case studyThe synthetic example is based on a gas reservoir in the North Sea (Figure 2) in which a clean sand reservoir saturated with gas lies between 2100 and 2250 m. The question we wish to answer using geophysical methods is: What is the gas satura-tion of the reservoir away from the well location?

The left side of Figure 2 shows the effect on the seismic response of changing the gas saturation, plotted in the P-impedance–Poisson’s ratio domain, for a water-saturated res-ervoir (blue), a reservoir containing a low saturation of gas (20% = red) and an 80% saturation (brown). It is clear that

the seismic response can detect the presence of the gas; there is good separation between the responses in the water-saturat-ed and gas-saturated cases. However, this does not answer the original question because the responses of the reservoirs satu-rated at 80% and 20% are almost identical. Although seismic data can be used to detect the gas, it cannot usefully constrain the saturation. The conclusions are the same when the AVA response is considered. Whereas there is a large AVA effect caused by the presence of the gas, the difference between the responses at low and high saturations is small, and would cer-tainly be hard to identify in real data (the well known fizz-gas problem).

Turning now to resistivity, there are a number of models describing the electrical properties of rocks saturated with flu-ids (e.g., Han et al., 2011; Gelius and Wang, 2008; Carcione et al., 2007; Waxman and Smits, 1968; Hashin and Shtrik-man, 1962). In this case, we use the simplest of these, Archie’s law (Archie, 1942), an empirical equation describing the rela-tionship between bulk resistivity of a two phase medium, RT, and its porosity φ, water saturation Sw and the resistivity of the interstitial fluids, Rw. The constants a, m, and n (respec-tively, the tortuosity, cementation exponent, and saturation exponent) are empirical, and must be calibrated on a case-by-case basis from well-log data.

Figure 2. (left) Well-log suite from a North Sea gas reservoir. The effect of fluid substitution on VP , VS, and density is shown in brown (for 80% gas saturation) and red (for 20% gas saturation). (right top panel) The effect of gas saturation plotted in the P-impedance–Poisson’s ratio domain. Blue shows the response when the gas is water wet, red the response for a fizz gas (20% saturation), and brown for a commercial 80% saturation. (right bottom) AVA response for the same three cases.



Bulk resistivity, RT, derived from Archie’s law is plotted in Figure 3 against hydrocarbon saturation for a range of porosi-ties. Taking the 25% porosity curve as an example, Figure 3 shows that the resistivity varies between about 1 Ωm for a saturation of 20%, up to about 25 Ωm when the gas satura-tion in the reservoir is 80%. This large difference in physical properties clearly would distinguish between low and high saturations if it could be measured remotely. However, note that there is a trade-off between saturation and porosity. A low-saturation, low-porosity medium will have the same re-sistivity as one with a much higher porosity and higher satu-ration.

Measurements of resistivity can be made using a variety of geophysical methods. Here we concentrate on the marine controlled-source electromagnetic (CSEM) method (Con-

stable and Srnka, 2007, provide a review of the method). The marine CSEM method uses a high-powered source to trans-mit a low-frequency signal through the sea floor to an array of receivers. Signal frequencies in the range 0.01–10 Hz are typically employed. By studying the variation in the received fields as a function of source-receiver separation, geometry, and signal frequency, the resistivity of the subsurface can be determined to depths of typically about 3 km below mudline (depending on the structure and resistivity of the overbur-den).

Figure 4 shows the sensitivity of a CSEM data set to changes in gas saturation for the same reservoir as above. The sensitivity is shown by plotting the difference between the inline electric field amplitude calculated for the gas-saturated reservoir compared to that calculated when the reservoir is water wet. This is plotted as a function of source-receiver separation (between 500 m and 20 km) and signal frequency (between 0.01 Hz and 5 Hz), covering the range of acquisi-tion parameters typically used in the field.

Looking first at the plot for the low gas-saturation (20%) case, it is clear that the effect of the gas on the CSEM response is small (at most 3%). Such a small difference would not be

resolved in survey data, and so a fizz-gas-saturated reservoir would be indistinguishable from a water-wet reservoir using CSEM data alone. In contrast the high gas saturation (80%) has a large effect on the CSEM response (>15%). Returning to the geo-physical question we are trying to answer (What is the gas saturation?), it is clear that whereas the seis-mic method can identify the presence of gas (but not its saturation), the CSEM method should be sensitive to the difference between low and high saturations.

However, although sensitivity to the property of interest is a necessary condition for successful application of the CSEM method, it is not suffi-cient. It is also important that this property can be recovered from a survey data set which is finite, noisy, and subject to error. Conversion of a pro-cessed CSEM data set into interpretable resistiv-ity sections or volumes relies inversion approaches that are themselves nonunique and subject to un-

certainty, and must be carefully constrained to ensure a mean-ingful answer. This will be illustrated using a synthetic data set generated from a simplified 1D model of the gas reservoir shown in Figure 2, which has been block averaged to provide a background resistivity structure. Embedded in this is a 25% porosity reservoir, which has a resistivity of 25 Ωm when sat-urated with 80% gas, and 2 Ωm when water-saturated. From this simplified model, synthetic data at 0.1 Hz, 0.3 Hz and 0.7 Hz are generated and contaminated with realistic survey noise. These data can then be inverted to establish how ac-curately, and under what conditions the saturation in the res-ervoir can be determined. In this case, the Occam inversion algorithm described by Constable et al. (1987) is used.

The left panels of Figure 6 show an unconstrained inver-sion of the synthetic data shown in Figure 5, along with the

Figure 3. Variation of resistivity with hydrocarbon saturation for a range of porosities, calculated from Archie’s law using typical parameters of m = n = 2 and a = 1.

Figure 4. Sensitivity of the CSEM method to a change in gas saturation for the reservoir shown in Figure 2. Sensitivity is calculated as the percentage difference between the inline electric field response for the gas-saturated reservoir, compared to the water-wet case, and is contoured as a function of source-receiver separation and signal frequency. The white solid contour shows a typical noise floor for the water depth considered here (1 km). Areas of the plot above and to the right of this line are below the noise floor and would not be usable in survey data. (left) 20% gas saturation. (right) 80% gas saturation.



corresponding inversion of data generated from a background model in which the reservoir was water-wet for comparison. The inversion does a good job of recovering the smoothly varying background resistivity trend. However, in the gas- saturated case the inversion returns a broad area of elevated resistivity, approximately centered on the known position of the reservoir, but with a peak resistivity considerably lower than in the input model. Because this feature is not present in

the background inversion, we can have confidence in attrib-uting it to the high resistivity associated with the gas. How-ever, if the background model were not known (in each case the odd harmonics of the fundamental were also available for processing), interpretation of the result would be much less clear. Is the elevated resistivity shown in the upper left panel of Figure 7 the result of a localized hydrocarbon accumula-tion, or is it perhaps the result of a more dispersed variation in porosity or lithology? Without further information, the in-terpretation risk of an unconstrained inversion result such as this would be high.

In most situations where CSEM data are acquired, seis-mic data are also available, and can be used to constrain structure in the CSEM inversion. Whereas an unconstrained inversion can only answer the question What is the resistivity of the Earth?, a poorly posed problem, inclusion of structural information from seismic modifies this to the better posed question What is the resistivity within a seismically defined structure? Constrained inversions for both the gas-charged and background cases are also shown in Figure 6.

The center panels of Figure 6 show the result when the depth of top reservoir is assumed constrained by seismic, and is therefore included as a break in the smoothness constraint in the inversion. This constraint has little effect on the inver-sion for the background structure (lower panels) indicating that such constraints will not create structure where there is none. In the gas-saturated case, the introduction of structural constraints localizes the recovered resistivity to the depth of the known structure. Such a result would provide a clearer indication of high resistivity within this structure.

The right panels of Figure 6 show the results when the depths of both the top and bottom of the reservoir are con-strained by seismic. For the gas-saturated case, the presence, depth, and resistivity of the gas-charged zone are now clearly resolved. When the structure is known, providing quantita-tive constraints on the resistivity properties within that struc-ture becomes possible.

The models shown in the top row of Figure 6 are math-ematically equivalent and based solely on the CSEM data and inversion; we have no reason to prefer one over the other. However, the geological interpretation resulting from each model is likely to be significantly different. So we must ques-tion what is actually constrained in the inversion process? The answer is that the parameter best constrained is the trans-verse resistance, defined as the vertically integrated resistivity across an interval (Harris et al., 2009). After calculating the transverse resistance across the interval of elevated resistivity in the unconstrained inversion result and comparing it to the transverse resistance calculated over the same interval for the true model, the inversion gives a result accurate to approxi-mately 12%. Doing the same comparison for the fully con-strained model gives a result accurate to 5–6%. In both cases, the transverse resistance is well constrained; however, with good structural constraint from seismic the accuracy of the recovered transverse resistance, and hence any rock or fluid properties derived from it, improves considerably. This will be illustrated next using a case study from the North Sea.

Figure 5. (left) Simplified 1D model representing the gas reservoir and surrounding structure. The reservoir has a resistivity of 25 Ωm when saturated with 80% gas, and 2 Ωm when water-saturated. (right) Synthetic CSEM amplitude and phase data generated from this model at three frequencies, and contaminated with realistic survey noise.

Figure 6. Inversion of synthetic data shown in Figure 5. In each case, the inversion result is shown in red and the true model in dashed gray. (top) Gas-charged reservoir (80% saturation). (bottom) Water-saturated background model. (left) Unconstrained inversion results. (center) The top of the reservoir is assumed known from seismic and is included as a smoothness break. (right) Both top and base reservoir interval are assumed constrained by the seismic and are included.



Determining the fluid in a North Sea chalk layerThe second example comes from a chalk reservoir in the North Sea. The area of porous chalk forming the reservoir interval lies in the top 30–40 m of the chalk layer, which itself varies between 100 and 500 m in thickness and lies approximately 2.5 km below sea floor. Although seismic data can be used to determine the structure and porosity of the chalk, they are extremely insensitive to the fluid type and distribution within it, because the impedance of porous water-saturated chalk is almost the same as that of porous oil-saturated chalk.

Figure 7 shows a well log through the chalk layer, pen-etrating areas of porous oil-saturated, porous water-saturated, and tight chalk. Histograms illustrating the distribution of resistivity in each of these three zones are also shown. The average bulk resistivity of the porous chalk is considerably higher in the presence of hydrocarbons. However, resistivity alone remains ambiguous, because the distribution of resistiv-ity within a tight chalk is extremely similar to that in a porous hydrocarbon-bearing chalk. There is a trade-off between po-rosity and saturation that cannot be resolved on the basis of resistivity measurements taken in isolation.

Given both impedance and resistivity measurements (pro-vided respectively by seismic and CSEM data), this ambigu-ity can be resolved. The seismic data can be used to identify porous areas of the chalk. Once the porosity is constrained, the CSEM data can be used to establish whether porous areas identified correspond to a high resistivity (potentially indicat-ing hydrocarbon charge) or low resistivity.

The CSEM survey layout is shown in Figure 8. The analy-sis presented here concentrates on line 2, a line trending NE-SW that passes from an area characterized by low-to-inter-mediate porosity chalk to an area of higher-porosity chalk at its northern end. Two source tows along this line were per-formed, the first at a fundamental frequency of 0.05 Hz and

the second at a fundamental frequency of 0.2 Hz (within each case, the odd harmonics of the fundamental also available for processing). The water depth in the survey area was 30–40 m.

The interpretation of CSEM data proceeds in stages, starting with simple 1D approaches and gradually adding complexity as required to explain the data. For this survey, the results of the 1D reconnaissance modeling stages indi-cated that the overburden structure (from sea floor down to the top of the chalk) was laterally continuous along the line. Within the chalk, however, the resistivity increases gradually to the north.

The next stage in the interpretation is to apply a 2.5D inversion. Seismic structural constraints on the depths of the top and base of chalk were included in the CSEM inversion as breaks in smoothness. The resulting resistivity section is shown in Figure 9. There is a clear increase in resistivity to-ward the northern end of the chalk layer, confirming the re-sults of the 1D modeling. The transverse resistance between the top and base chalk is also shown in Figure 9, and again increases to the northern end of the line. However, the cause of the increase in resistivity remains ambiguous when only the CSEM data are considered. It could be the result of a decrease in porosity of the chalk, a change in the chalk thick-ness or a change in the fluid content of the chalk from water to more resistive hydrocarbons. By incorporating the seismic data in the analysis, this ambiguity can be resolved.

Seismic data can be used to constrain the porosity of the chalk layer. The left panel of Figure 10 shows the result of inversion of the seismic data along the CSEM line calibrated to porosity. The high-porosity zone in the upper few tens of meters of the chalk forms the reservoir interval, underlain by a much lower-porosity section.

Applying Archie’s law and assuming 100% water satu-ration allows us to convert the seismically derived porosity into an equivalent seismically derived resistivity (Figure 10).

Figure 7. (left) Well-log data from the chalk layer. Tracks show resistivity, density, porosity, lithology, and water saturation. (right) Histograms showing the distribution of resistivity within porous oil-charged chalk, porous water-wet chalk, and tight chalk.

Figure 8. Map showing the layout of the CSEM survey. Black circles represent receiver locations, and the source tow line is shown by the continuous black line, which also extended 10 km outside the receiver array (not shown). The colors represent the average porosity in the upper 24 m of the chalk layer, derived from calibrated inversion of seismic data. Note the salt diapir in the southwest corner of the survey area.



Figure 9. (bottom) 2D resistivity section resulting from inversion of 0.05 Hz, 0.25 Hz and 0.6 Hz amplitude and phase data from CSEM line 2. Structural constraints from seismic data are included as breaks in the smoothness constraint at the top and base of the chalk. The resistivity within the chalk increases toward the northern end (right side in the figure) of the line. (top) Transverse resistance between the top and base chalk calculated from the CSEM inversion result. This parameter is well constrained by CSEM data.

Figure 10. (left) Porosity section within the chalk along line 2, derived from inversion of seismic data. (middle and right) The low-baseline and high-baseline (respectively) seismically derived resistivity, calculated from the porosity section using Archie’s law calibrated at wells in the area, and assuming 100% saturation with water. Low-baseline case assumes Rw = 0.04 Ωm and the high-baseline case assumes Rw = 0.08 Ωm.

The empirical Archie parameters are calibrated using well-log data from the survey area to give a value of the cementation parameter, m=1.6. The interstitial water resistivity, Rw, varies across the survey from values below 0.04 Ωm in areas close to the salt diapir where pore fluids are likely to be more saline, to values above 0.08 Ωm. Given the significant variation in Rw, low- and high-baseline resistivity sections were calculated (Figure 10, middle and right). The seismically derived trans-verse resistance in the chalk can then be calculated as before (Figure 11, blue curves). This reflects the effect of variations in porosity and chalk thickness. Any deviation from this curve is therefore indicative of a change in pore-fluid proper-ties or type. This is also illustrated in Figure 11, which shows the equivalent transverse resistance when a synthetic hydro-carbon reservoir, consistent with horizontal logs through the known hydrocarbon accumulation in the area, is included in the model (red and pink curves for the low- and high-baseline curves, respectively).

By comparing the CSEM-derived transverse resistance with the seismically derived values, the effects of fluids in the chalk can be isolated. This comparison is shown by black in Figure 11. At the southern end of the line, outside the area known to be charged with hydrocarbons, the CSEM result follows the low-baseline seismically derived curve closely. This area also corresponds to the portion of the line closest to the salt diapir, so a low Rw is plausible if the pore fluids have a higher salinity than elsewhere as a result. The well log from which the synthetic reservoir was constructed runs between 14 and 19 km along the line. In this area, the CSEM result follows the high-baseline seismically derived curve closely. Outside this area (from 19 to 25 km along the line), the large difference between the CSEM and seismically derived trans-verse resistance suggests an extension to the north of the hy-drocarbon accumulation, either beneath or adjacent to the line.

There are often ambiguities in the interpretation of a sin-gle geophysical data type. Here we have illustrated this with a case study from a North Sea chalk layer. Seismic data can be used to identify porous areas in the chalk, but cannot distin-

guish between water-wet and hydrocarbon-charged chalk. In contrast CSEM data can be used to identify areas of high re-sistivity; however, these may be caused either by low-porosity zones, or by porous zones that are charged with resistive hy-drocarbons. By integrating the analysis of the two data types, the ambiguities in each can be mitigated, providing in this case an effective indication of the fluid content of the chalk.

The futureThe ultimate goal of a geophysical analysis is to find a con-strained model of geology, lithology, and fluid properties on which commercial decisions about reservoir exploration, de-velopment, and management can be based. To achieve this, the Earth can be interrogated with a number of tools. In this paper we have considered seismic, CSEM, and well-log data. However, gravity data, magnetic data, and magnetotel-lurics (for example) also have an important role to play (De Stefano et al., 2011; Jegen et al., 2009). Each data type must



be interpreted within an integrated framework so that the resulting Earth model is consistent with all the data used in its construction.

Within this integrated framework, CSEM can provide valuable complementary information. However, there are a few considerations in applying this method to ensure that it delivers its full potential. The first, and perhaps most impor-tant, point is to ensure the geophysical question to which the CSEM method is applied is well posed and suitable for the technique. If the question is poorly posed, the answer will be equally poorly constrained, and interpretation risk of the result will be high.

Good survey design is essential. Acquisition parameters such as required offsets and transmission frequencies are criti-cal to the success of a CSEM survey. Survey parameters must be carefully targeted to answer the question posed above. If they are not, then the survey may fail to be sensitive to the structures of interest, leading to a disappointing result. In or-der to design the survey correctly, a good background model is required. CSEM survey parameters depend critically on factors such as overburden structure, resistivity, and anisot-ropy, and therefore constraint on these is critical to a well designed survey.

The applicability of CSEM methods must be considered with this in mind. CSEM methods can in principle be ap-plied at any stage during the life cycle of an oil field, although the question being addressed varies:

• Frontier exploration: What is the resistivity structure of the Earth?

• Exploration: What is the resistivity within the seismically defined structure?

• Appraisal: Given the seismic structure and the properties of the reservoir at a well, how do these properties vary across the field?

• Monitoring: Given the seismic structure, and properties in the well, how do these properties vary across the field and through time?

The interpretation uncertainty and risk associated with CSEM declines as more information is available to construct an integrated interpretation. Therefore, of these possible ap-plications, the last three, where both well and seismic data are available, will produce results with the lowest uncertainties. To date, the CSEM industry has been focused on exploration, with only a few surveys targeted at appraisal, and none direct-ly at monitoring, for which the technology may be much bet-ter suited. Perhaps in the future, the most widely applied use of CSEM methods will shift toward these applications. In-deed several recent studies (for example Orange et al., 2009; Andreis et al., 2011; Liang et al., 2011) have demonstrated the potential power of CSEM methods as a complement to seismic in monitoring applications.

ConclusionsThere are many geophysical questions that cannot be an-swered unambiguously (or at all) by a single data type. The examples presented here illustrate situations where integra-tion of seismic, well and CSEM data, exploiting the strengths of each, is required. Many other examples, incorporating a variety of data types, exist. The field of data integration and shared Earth modeling is large, and rapidly expanding with technology moving from qualitative to quantitative ap-proaches. There are many challenges, for example combining

Figure 11. Comparison between the CSEM and seismically derived transverse resistance curves. Light blue and dark blue curves = high- and low-baseline seismically derived transverse resistance assuming 100% water saturation. Pink (high-baseline) and red (low-baseline) curves = same but with a synthetic hydrocarbon reservoir based on horizontal well-log information included. Black curve = CSEM-derived transverse resistance. In the southern (left) and central part of the line, the CSEM result agrees well with the seismic and well-log predictions. At the northern end of the line, where no well log is available, the large difference between CSEM and seismically derived transverse resistance suggests an extension of the resistive hydrocarbon layer to the north.



different physical properties, using technologies that sense the Earth at a range of different scales is not straightforward. However, there are also many opportunities, if these chal-lenges can be overcome, to improve the quality and con-straints of geophysical information on which commercial decisions are based.

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Acknowledgments: I thank my colleagues at RSI for their help,

especially David Andreis, Paola Vera de Newton, Uwe Strecker, and Anyela Morecote who all contributed to the work presented. The North Sea case study data were acquired by OHM Ltd., and thanks to Maersk, Chevron, and Shell for permission to publish the case study. Many of the workflows described were developed under the WISE consortium project, sponsored by Maersk, Chevron, Det Norske, Total, DONG, and the UK’s Department for Energy and Climate Change (DECC). The time-lapse modeling included in the lecture was performed as part of a research project in collabora-tion with BP, and funded by the UK Technology Strategy Board. Finally, thanks to SEG for organizing the Honorary Lecture program, and to Shell for its generous sponsorship.

Corresponding author: [email protected]

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