prediction of performances of methane hydrate production tests in

PREDICTION OF PERFORMANCES OF METHANE HYDRATE PRODUCTION TESTS IN THE EASTERN NANKAI TROUGH

Masanori Kurihara∗ Japan Oil Engineering Company

1-7-3 Kachidoki, Chuo-ku, Tokyo, 104-0054, Japan (at present, Department of Resources and Environmental Engineering, Waseda University)

Hisanao Ouchi and Akihiko Sato (Japan Oil Engineering Company)

Koji Yamamoto and Satoshi Noguchi (Japan Oil, Gas and Metals National Corporation)

Hideo Narita and Jiro Nagao

(National Institute of Advanced Industrial Science and Technology)

Yoshihiro Masuda (The University of Tokyo)

ABSTRACT The MH21 Research Consortium in Japan is planning to conduct offshore production tests targeting methane hydrate (MH) reservoirs located in the Eastern Nankai Trough. In the course of the design of the first production test scheduled in 2012, we have constructed the 3D reservoir models for two candidate areas; the vicinities of the α-1 and β-1 wells that were drilled in the 2004 exploratory drilling campaign. These models were constructed integrating the results of the seismic interpretation, well log interpretation and core analysis. A series of numerical simulations were then conducted using these models, to predict performances of the first offshore production test assuming a variety of well locations and test schemes. First of all, ten candidate locations were selected for the production test well, five (α-pt1 through α-pt5) from the α-1 area and five (β-pt1 through β-pt5) from the β-1 area, in view of the flow capacity and MH saturation suggested by the 3D reservoir models. The 2D radial models were then built extracting a part of the reservoir properties from the 3D reservoir model, in order to roughly investigate the difference in the production performance by location. The 30 day test performances were predicted assuming the application of the depressurization method with the bottomhole flowing pressure of 3 MPa as a base case. In addition, several case simulations were conducted to investigate the effects of bottomhole pressure, completion interval and absolute permeability of sand layers on test behaviors. Furthermore, four 3D sector models were constructed representing the small areas around the locations that showed the relatively higher gas production in the 2D radial model simulation. Using these sector models, the test performances were predicted more rigorously taking account of the effect of the faults, formation dip and lateral heterogeneity. Finally the production test schemes including the location of a test well were recommended comprehensively analyzing the results of the above simulation studies.

Keywords: methane hydrates, production test, numerical simulation, reservoir modeling ∗ Corresponding author: Phone: +81 3 5286 2697 Fax: +81 3 5286 3491 Email: [email protected]

Proceedings of the 7th International Conference on Gas Hydrates (ICGH 2011), Edinburgh, Scotland, United Kingdom, July 17-21, 2011.

INTRODUCTION Background The MH21 Research Consortium in Japan, which was organized to accomplish the exploration and exploitation of methane hydrate (MH) offshore Japan, has been implementing a variety of research projects toward the assessment of MH resources, establishment of MH production methods and examination of environmental impacts of MH development. As one of the key events in such research projects, the Consortium is planning to conduct offshore production tests targeting MH reservoirs located in the Eastern Nankai Trough and the first production test is scheduled in 2012. In the Consortium, we have been developing the state-of-the-art numerical simulator (MH21-HYDRES) for rigorously predicting MH dissociation and production behaviors. Using this numerical simulator, we have been predicting the gas producibility from the Eastern Nankai Trough MH reservoirs [1, 2] and the performances of production tests conducted in these MH reservoirs [3]. After these studies, however, some seismic data, log data and core analysis results were re-interpreted, providing new features of reservoir petrophysical properties. Reflecting these new features, we updated and expanded the 3D model constructed in our previous study [3]. Candidate locations for the production test well were then selected, in view of the flow capacity and MH saturation suggested by the 3D reservoir models thus updated. The 2D radial models and 3D sector models were built for the vicinity of these candidate test well locations, extracting a part of the reservoir properties from the 3D reservoir models, followed by the prediction of 30 day test performances assuming the application of the depressurization method. This paper describes the procedures of the update and expansion of the existing 3D MH reservoir models, reflecting the results of the re-interpretation of seismic data, log data and core analysis results. This paper also presents how much of gas and water production is expected during the production test and how reservoir/operation parameters such as sand layer permeability, completion interval and the bottomhole pressure affect the production.

MH reservoirs in the Eastern Nankai Trough In the Eastern Nankai Trough, the 2D and 3D seismic surveys were conducted in 2001 and in 2002, respectively. Furthermore, 6 exploration wells were drilled where logging-while-drilling (LWD) data, wireline logging data and core samples were acquired at Offshore Tokai in 1999 and these confirmed the presence of the interval with relatively high MH saturations [4]. Another exploratory drilling was accomplished targeting the broader area in 2004, where total of 32 exploration wells were drilled to obtain well log data and core samples at 16 locations at Kumano basin, Daini Atsumi Knoll and Offshore Tokai [5] as shown in Figure 1. Japan Oil, Gas and Metals National Corporation (JOGMEC) has been analyzing the data thus acquired during these seismic surveys and exploratory drillings, aiming at the estimation of the initial methane in place in MH reservoirs located in the Eastern Nankai Trough. In the course of these analyses, it has been revealed that the MH reservoirs are composed of the sequence of thin layers (mostly less than 0.4 m) of sand, silt and mud within turbidite beds. The saturation of MH is negligibly small in mud layers, even if MH is contained in these layers.

Figure 1 METI 2D/3D seismic surveys and

exploratory well drilling in the Eastern Nankai Trough

In addition, it has been confirmed that there exist the MH concentrated zones showing the resistivity higher than about 3 ohm-m, in the regions satisfying the following conditions [6].

• The bottom simulating reflector (BSR) exists. • Turbidite sand layers exist.

• Strong seismic reflectors are observed. • Relatively high interval velocity is observed.

We selected five locations near α-1 exploratory well in the α MH concentrated zone and another five locations near β-1 exploratory well in the β MH concentrated zone as candidates for the first offshore production test well location. The α MH concentrated zone is composed of lobe type turbidite deposits, while the β MH concentrated zone is composed of channel type turbidite deposits. This must be a good selection for examining the differences in reservoir modeling and in simulation between lobe type and channel type turbidite deposits. Since the α MH concentrated zone is composed of the lobe type turbidite deposits, the connectivity of reservoir layers seems to be relatively good and hence the changes in reservoir properties such as porosity and permeability in the lateral direction within reservoir layers seems to be relatively small. Furthermore, the dip of each reservoir layer ranges between 4 and 6 degree, reflecting the gentle deposit at the location distant from a submarine channel. The vicinity of the α-1 well, however, is divided into multiple blocks by faults, which is one of the major sources of heterogeneity in this area. On the other hand, the β MH concentrated zone consists of the channel type turbidite deposits, it is indicated by the seismic wave amplitude that the connectivity of sand layers seems to be relatively poor and that parts of the sand layers are altered with silt and mud layers. Furthermore, the dip of each reservoir layer is high (about 20 degrees), due to the effect of the uplift event in this area. There are no major faults detected in the vicinity of the β-1 well. Numerical simulator The simulator used in this study (MH21-HYDRES) was originally developed by the University of Tokyo and has since been modified and improved by Japan Oil Engineering Co., Ltd., the University of Tokyo, Japan National Oil Corporation and National Institute of Advanced Industrial Science and Technology (AIST) [7, 8. 9]. This simulator is able to deal with three-dimensional, five-phase (gas, water, ice, MH and salt (deposit)), six-component (methane, carbon

dioxide, nitrogen, water, methanol and salt) problems. Further details on this simulator are given in our previous papers [7, 8. 9]. STUDY FOR α MH CONCENTRATED ZONE The overall procedure of the study for the α MH concentrated zone is illustrated in Figure 2. In our previous study, the 3D reservoir model was constructed for the vicinity of the α-1 well incorporating 5 fault blocks covering the 1.1 km (east-west direction) x 1.0 km (north-south direction) area [3]. In this study, this model was modified and expanded to the area of 1.5375 km (east-west direction) x 1.0 km (north-south direction) for the vicinity of the α-1 well, reflecting the updated geological information including the relation between seismic attributes and MH saturation.

Seismic data

Well log data

Core data

3D geological frame model

(horizons, faults, etc.)

Layering for frame inside

Estimation of layer property distributions (φ, Sh, k, etc.)

Geostatistical techniques

Wav

e re

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ion,

am

plitu

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tc.

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3D reservoir model

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Selection of candidate locations for test wells

2D radialmodel

3D sector model

Base case simulation

Case/sensitivity simulation

Base case simulation

Case/sensitivity simulation

extraction extraction

Figure 2 Study procedure for α MH concentrated

zone Candidate locations for the production test well were then selected in view of the flow capacity and the MH saturation suggested by the 3D reservoir model. The 2D radial models were then built extracting a part of the reservoir properties from the 3D reservoir model. In addition, the 3D sector models were also constructed. Test performances were predicted using these models. Reservoir modeling In the first stage of reservoir modeling, geological data including seismic data, well log data and core analysis results were re-interpreted. The geological frame model for the target area was then re-constructed and expanded based mainly on the horizons identified by seismic interpretation. The inside of this geological frame model was

divided into thin reservoir model layers, the distributions of the properties of which were estimated using geostatistical techniques. The reservoir model was completed by estimating the distributions of initial pressure and temperature. Geological data analysis JOGMEC re-interpreted the log data of the α-1 well using not only LWD data but also wireline log data. JOGMEC also expanded the area for identifying the seismic horizons from the fault block (between F2 and F3 faults) where the α-1 well exists to the neighboring fault block in the north (between F3 and F4 faults). Furthermore JOGMEC suggested the relation between seismic wave velocity and MH saturation as shown in Figure 3 [10].

MH saturation Figure 3 Relation between seismic wave

velocities and MH saturation

Core Permeability vs. Core Porosity

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Figure 4 Relation between absolute permeability and porosity estimated from core analysis results

On the other hand, AIST re-interpreted the core analysis results to clarify the relationship between porosity and absolute permeability. Figures 4 presents the absolute permeability for various facies related to the porosity by the Kozeny-Carman model. Construction of geological frame model The 3D geological frame model was re-constructed for the vicinity of the α-1 well (i.e., 1.5375 km in the east-west direction and 1.0 km in the north-south direction around the well). The grid blocks in the shape of a square 12.5 m long in the lateral direction, which is equivalent to the interval of seismic lines, were used for this model. In this modeling, the depths of 14 horizons, seabed and BSR identified by the 3D seismic interpretation were adjusted, correlated with the well log interpretation results. The locations of the 4 faults incorporated in this model were also specified in terms of the coordinates of the surfaces of these faults. Construction of 3D reservoir model The above geological model was then converted to the 3D reservoir model by 1) specifying the reservoir layers inside the frames, 2) estimating the lithology of each layer, 3) estimating the distributions of the properties for each reservoir layer, 4) refining grid blocks, 5) adding overburden and underburden grid layers and 6) specifying initial pressure and temperature. Four hundred ninety four (494) reservoir layers, namely the sand, silt and mud layers confirmed at the location of the α-1 well by the updated well log interpretation, were allocated to the inside of the geological frames as shown in Figure 5. These 494 layers include 44 layers added on the top of the MH interval confirmed at the α-1 well location, because the strong wave reflection was detected in these 44 layers suggesting the existence of MH in the fault block between F3 and F4 faults while these 44 layers do not contain MH at the α-1 well location. These layers confirmed at the α-1 well were assumed to be extended throughout the model area because the continuity of the reservoir layers was considered to be good at the center of the lobe type turbidite deposit. The change in the thickness of the reservoir layer was estimated in proportion to the change in the thickness of geological frames.

For the geological frames that do not exist at the α-1 well location but appear at the locations distant from the well, the distributions of 73 reservoir layers were estimated simply extracting the reservoir layers confirmed within the interval at the α-1 well location where the thickness of sand layers are almost the same as that of mud layers.

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Figure 5 Petrophysical properties for each grid

layer estimated by updated interpretation of well log data and core analysis results (at α-1 well)

The distributions of the properties of each reservoir layer were estimated based on those confirmed at the α-1 well location as listed in Table 1. The lithology (sand, silt and mud) of each layer were considered to be unchanged throughout the model area. The porosity distributions were estimated using the geostatistical method of sequential Gaussian simulation (SGS), conditioning the estimates to the porosity values at the α-1 well location. In estimating the water saturation distributions and hence the MH saturation distributions using SGS, the saturation values at the α-1 well location were used as hard data primarily conditioning the distributions, while the seismic wave velocity was used as soft data taking account of the relationship between MH saturation and wave velocities shown in Figure 3. The absolute permeabilities of sand, silt and mud layers were estimated based on the facies and the porosity using the correlation presented in Figure 4. The distributions of the initial effective permeability to water in the presence of MH were estimated using SGS, conditioning the estimates to the initial effective permeability values at the α-1 well location.

Although the grid blocks allocated to the above model are very small, they are still not fine enough to rigorously predicting the MH dissociation and fluid flow behaviors. Therefore, sand and silt layers containing MH were sub-divided into two thinner grid layers, which resulted in the total of 804 grid layers from 494 reservoir layers and the additional 73 layers defined in the above. In addition, 4 grid layers were added on the top of the model mimicking the sea water and mud layers located between the sea floor and the MH zone. Another 3 grid layers were added below the bottom of the model to express the 60 m thick mud layer existing below the MH interval. These refinements and addition of grid blocks lead to the total number of effective grid blocks of 7,301,964 (Table 1). Initial reservoir pressure distribution was estimated assuming the hydrostatic gradient of water with the salinity of 35,000 ppm. The initial reservoir temperature distribution was estimated assuming that the temperature at the BSR was equal to the MH-methane-water equilibrium temperature and that the temperature gradient was 0.03 K/m. Figure 6 shows the 3D reservoir model thus completed.

Modeling area 1,537.5 m x 1,000 m (east-west direction x north-south direction)

Modeling Interval From the seafloor to 107 m below the MH concentrated zone at α-1 well

Grid system 3 dimensional corner point grid

Number of grids Number of grids: 7,980,240 (= 123x80x811) Number of effective grids: 7,301,964

Lithology Same as the lithology estimated at α-1 well (by well log re-interpretation) throughout each reservoir layer

Porosity Estimated by Sequential Gaussian Simulation (SGS), conditioning to the porosity value at α-1 well

Absolute permeability Estimated based on the lithology and the porosity of the grid block using correlation

Relative permeability to gas and water

Expressed as cubic polynomial (irreducible water saturation: 27 %, critical gas saturation: 3 %)

Initial effective permeability to water

Estimated by SGS conditioning to the value of initial effective permeability to water at α-1 well

Initial MH saturation Sh=1-Sw

Initial water saturation

Sand and silt layers: estimated by SGS using water saturation at α-1 well as hard data and seismic attributes as soft data Mud layers: constant at 99%

Rock thermal conductivity

2.915 W/m/K (sand and silt layers), 1.7 W/m/K (mud layers)

Initial pressure Estimated assuming hydrostatic pressure gradient of 35,000 ppm saline water)

Initial temperature

Estimated assuming that the initial temperature is equivalent to the 3 phase (methane-water-MH) equilibrium temperature at the depth of BSR and that the geothermal gradient is 0.03 K/m

MH interval at α-1 well (True Vertical Depth) 815.4-920.2 m (104.8 m)

Table 1. Specifications of 3D reservoir model for α MH concentrated zone

1.5 km1.0 km

α-1 fault block

α-1 north fault block

α-1

Fault

North

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~8,000,000

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Figure 6 3D model for vicinity of α-1 well

Construction of smaller scale reservoir models As shown in Figure 7, the flow capacity of the reservoir tends to increase toward the north-east direction of the α-1 well. Parts of region in the west from α-1 well also have slightly higher flow capacity. Five (5) candidate locations (α-pt1 through α-pt5) were selected for the production test well as shown in Figure 7. 2D radial models composed of about 85,000 grid blocks were then built for these 5 locations, extracting the reservoir properties assigned to each location in the 3D reservoir model. Therefore, these radial models have flat grid layers and the homogeneous model properties in the lateral direction as shown in Figure 7.

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Total flow capacity distribution assigned to the 3D reservoir model and candidate locations for test wells

Construction of 2D radial models extracting reservoir properties

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Figure 7 Flow capacity distribution for selecting

locations for 2D radial models (α MH zone) Furthermore, as illustrated in Figure 8, the vicinity of the α-pt1 and α-pt3 locations were extracted from the 3D reservoir models to construct the 3D sector models having about 1,500,000 grid blocks. Since it is reasonable and practical to conduct the production test in the same fault block of the α-1 well from the viewpoint of the reliability of the estimation of reservoir properties, these 2 locations were selected for the further investigation using

the 3D sector model that can reflect the effects of lateral heterogeneity, reservoir dip and faults.

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Construction of 3D sector models extracting reservoir properties

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locations for 3D sector models (α MH zone) Prediction of test performances The test performances were predicted with 2D radial and 3D sector models, assuming the application of the depressurization method for 30 days. In addition to the base case simulation, several case simulations were conducted to investigate the effects of the uncertain reservoir properties and operation conditions such as absolute permeability, completion interval and bottomhole pressure. The results of these simulations are summarized in Tables 2 and 3 for the radial model study and sector model study, respectively.

1,000 mDshorter completion3 MPa1000mD-perf

1,000 mDalmost full for MH interval4 MPa1000mD-4MPa

1,000 mDalmost full for MH interval3 MPa1000mD-base

estimated from φ-k correlation

shorter completion 3 MPaperf


almost full for MH interval4 MPa4MPa


almost full for MH interval3 MPabase

Absolute permeability of

sand layers

Completion interval

Bottomhole pressureCase

1,000 mDshorter completion3 MPa1000mD-perf

1,000 mDalmost full for MH interval4 MPa1000mD-4MPa

1,000 mDalmost full for MH interval3 MPa1000mD-base


shorter completion 3 MPaperf


almost full for MH interval4 MPa4MPa


almost full for MH interval3 MPabase

Absolute permeability of

sand layers

Completion interval

Bottomhole pressureCase

Table 4. Case conditions

2D radial model study In the base case simulation, it was presumed that the test well would complete almost full interval of the MH concentrated zone (~100 m) , that the bottomhole pressure would be reduced down to 3 MPa and that the absolute permeability of each layer could be estimated based on the porosity-permeability correlation shown in Figure 4. In addition to the base case

simulation, several case simulation runs were executed as shown in Table 4 in order to investigate the effects of absolute permeability of sand layers, bottomhole pressure and completion interval. It was assumed in all the cases that all the faults were sealed. Figure 9 depicts the gas and water production for 30 days predicted for each candidate test well location as well as for the α-1 well in the base case. The α-1 well showed the highest gas and water production rates (approximately 20,000 m3/d and 500 m3/d, respectively) among the candidate test well locations, although the flow capacity around this well is estimated to be the lowest. In the case of the candidate test wells located in the region of higher flow capacity, the initial MH saturation was also inferred to be higher in accordance with the seismic wave velocities, which resulted in the smaller initial effective permeability to water and hence in the smaller MH dissociation and gas production.

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As summarized in Table 2, in all the candidate test well locations, the gas production was predicted to be about a half of that in the base case, if the completion interval became shorter (about 70% of the full completion). It was also predicted that the gas production became one quarter of that in the base case, if the bottomhole pressure was reduced only to 4 MPa. In the cases that the absolute permeability of sand layers is as high as 1,000 mD, the gas production was predicted to increase by the factor of 4 in comparison with the cases with the absolute permeability estimated from the porosity-permeability correlation. 3D sector model study As mentioned in the above, the vicinities of α-pt1 and α-pt3 locations were selected for the 3D sector model study (Figure 8), because these locations exist within the same fault block of the α-1 well and showed relatively high gas producibilty in the radial model study. In these sector model studies, the case conditions were set to be exactly the same as those in the radial model studies; namely, the base case and the cases for investigating the effects of absolute permeability of sand layers, completion interval and bottomhole pressure. As summarized in Table 3, the gas and water production predicted in the sector model studies was very close to that in the radial model study. Figure 10 depict the oil and gas production for the α-pt3 location predicted in various cases. Figure 11 presents the cross sectional views of the distributions of pressure, temperature, MH saturation and gas saturation after 30 days from the commencement of the test, predicted in the base case for α-pt3 location. As shown in this figure, the area of main MH dissociation extends to 30-40 m from the well and hence neither pressure effect nor MH dissociation front reached the faults during 30 days. Since the small scale of heterogeneity such as small changes in reservoir properties may not significantly affect the short term test performances, the results of the 3D sector model studies became similar to those of the radial models studies, without being affected by the large scale heterogeneity of faults.

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Figure 11 cross sectional views of reservoir

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STUDY FOR β MH CONCENTRATED ZONE The overall procedure of the study for the β MH concentrated zone is almost the same as that for the α MH concentrated zone illustrated in Figure 2. In our previous study, the 3D reservoir model was constructed for the square area with side 1 km long around the β-1 well [3]. In this study, this model was modified and expanded to the area of 1.0 km (east-west direction) x 1.5625 km (north-south

direction) for the vicinity of the β-1 well, reflecting the updated geological information including the boundaries of submarine channel bodies suggested by seismic wave reflectors. As in the study for the α MH concentrated zone, the candidate locations of production test well were then selected in view of the flow capacity and the MH saturation suggested by the 3D reservoir model. The 2D radial models were then built extracting a part of the reservoir properties from the 3D reservoir model. In addition, the 3D sector models were also constructed. Test performances were predicted using these models. Reservoir modeling In the first stage of reservoir modeling, geological data including seismic data and well log data were re-interpreted. The geological frame model for the target area was then re-constructed and expanded based mainly on the horizons and the boundaries of channel bodies identified by seismic interpretation. The inside of this geological frame model was divided into thin reservoir model layers, the distributions of the properties of which were estimated using geostatistical techniques. The reservoir model was completed by estimating the distributions of initial pressure and temperature. Geological data analysis JOGMEC re-interpreted the log data of the β-1 well using not only LWD data but also wireline log data. JOGMEC also expanded the area for identifying the seismic horizons from 1.0 km x 1.0 km area in the vicinity of the β-1 well to the 1.0 km x 1.5625 km area. Furthermore JOGMEC suggested the distributions of the channel bodies as depicted in Figure 12.

β1-L

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Figure 12 Channel distributions traced by

JOGMEC

Construction of geological frame model The 3D geological frame model was re-constructed for the vicinity of the β-1 well (i.e., 1.0 km in the east-west direction and 1.5625 km in the north-south direction around the well). The grid blocks in the shape of a square 12.5 m long in the lateral direction, which is equivalent to the interval of seismic lines, were used for this model. In this modeling, the depths of 12 horizons, seabed and BSR identified by the 3D seismic interpretation were adjusted, correlated with the well log interpretation results. Furthermore, the boundaries of 4 channel bodies outlined by the seismic interpretation were specified in the geological frame model (Figure 12). Construction of 3D reservoir model The above geological model was then converted to the 3D reservoir model by 1) specifying the reservoir layers inside the frames, 2) estimating the lithology of each layer, 3) estimating the distributions of the properties for each reservoir layer, 4) adding overburden and underburden grid layers, and 5) specifying initial pressure and temperature.

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Figure 13 Petrophysical properties for each grid layer estimated by updated interpretation of well

log data and core analysis results (at β-1 well) One hundred twenty two (122) reservoir layers, namely the sand, silt and mud layers confirmed at the location of the β-1 well by the updated well log interpretation, were allocated to the inside of the geological frames as shown in Figure 13. These layers confirmed at the β-1 well were assumed to be extended throughout the model area, although the lithology may be altered (e.g., from sand to silt, from mud to silt, etc.) within a layer, reflecting the extent of channel bodies. The change in the thickness of the reservoir layer was

estimated in proportion to the change in the thickness of geological frames. Since the reservoir is inclined toward the north-west direction around the β-1 well with a dip of about 20 degrees, the layers located below BSR at the β-1 well location should appear above BSR and contain MH in the south-east part of the β-1 well. Hence, 572 thin grid layers corresponding to sand, silt and mud layers with total thickness of about 370 m were added below BSR detected at the β-1 well. The distributions of the properties of each reservoir layer were estimated based on those confirmed at the β-1 well location as listed in Table 5. The lithology (sand, silt and mud) distributions in each layer were estimated based on the lithology inferred at the β-1 well and on the distributions of channel bodies. The porosity distributions were estimated using SGS, conditioning the estimates to the porosity values at the β-1 well location. In estimating the water saturation distributions and hence the MH saturation distributions using SGS, the saturation values at the well location were used as hard data, and the seismic wave velocity and the distribution of the regions of high seismic wave amplitude as soft data taking account of the relationship between MH saturation and wave velocities shown in Figure 3. The absolute permeabilities of sand, silt and mud layers were estimated based on the facies and the porosity using the correlation presented in Figure 4, although this correlation was estimated based only on the core data acquired from the α-1 well. The distributions of the initial effective permeability to water in the presence of MH was estimated using SGS, conditioning the estimates to the initial effective permeability values at the β-1 well location. Unlike the case of the 3D reservoir model for the α MH concentrated zone, sand and silt layers containing MH were not sub-divided into thinner grid layers. Instead, grid blocks were locally subdivided in constructing the 2D radial models and 3D sector models extracting from the 3D reservoir model. Seventeen (17) grid layers were added on the top of the model mimicking sea

water and mud layers located between the sea floor and the MH zone. Another 3 grid layers were also added below the bottom of the model to express the 75 m thick mud layer existing below the MH interval. These addition lead to the total number of effective grid blocks of 7,140,000.

Modeling area 1,000 m x 1,562.5 m (east-west direction x north-south direction)

Modeling Interval From the seafloor to 373 m below the MH concentrated zone at β−1 well

Grid system 3 dimensional corner point grid

Number of grids Number of grids: 7,140,000 (= 80x125x714) Number of effective grids: 7,140,000

Lithology Same as the lithology estimated at β−1 well (by well log re-interpretation) throughout each reservoir layer

Porosity Estimated by Sequential Gaussian Simulation (SGS), conditioning to the porosity value at β−1 well

Absolute permeability Defined based on the lithology and the porosity of the grid block

Relative permeability to gas and water

Expressed as cubic polynomial (irreducible water saturation: 27 %, critical gas saturation: 3 %)

Initial effective permeability to water

Estimated by SGS conditioning to the value of initial effective permeability to water at β-1 well

Initial MH saturation Sh=1-Sw

Initial water saturation

Sand or silt layers: estimated by SGS using water saturation at β−1 well as hard data and seismic attributes as soft data Mud layers: constant at 99%

Rock thermal conductivity

2.915 W/m/K (sand and silt layers), 1.7 W/m/K (mud layers)

Initial pressure Estimated assuming hydrostatic pressure gradient of 35,000 ppm saline water)

Initial temperature

Estimated assuming that the initial temperature is equivalent to the 3 phase (methane-water-MH) equilibrium temperature at the depth of BSR and that the geothermal gradient is 0.03 K/m

MH interval at β-1 well (True Vertical Depth) 1,290.0-1,340.0 m (50.0 m)

Table 5. Specifications of 3D reservoir model for β MH concentrated zone

ENE

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Figure 14 3D model for vicinity of β-1 well

Initial reservoir pressure distribution was estimated assuming the hydrostatic gradient of water with the salinity of 35,000 ppm. The initial reservoir temperature distribution was estimated assuming that the temperature at the BSR was equal to the MH-methane-water equilibrium

temperature and that the temperature gradient was 0.03 K/m. Figure 14 shows the 3D reservoir model thus completed. Construction of smaller scale reservoir models As shown in Figure 15, there exists the region showing relatively high flow capacity in the south of the β-1 well. Five (5) candidate locations (β-pt1 through β-pt5) were selected from this region of high flow capacity for the production test well, as shown in Figure 15. 2D radial models composed of about 100,000 grid blocks were then built for these 5 locations, extracting the reservoir properties assigned to each location in the 3D reservoir model and refining the grid blocks near completion interval. Therefore, these radial models have flat grid layers and the homogeneous model properties in the lateral direction as illustrated in Figure 15.

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Selection of candidate locations for test wells from the viewpoints of flow capacity and continuity (distribution of channel bodies) with β-1 well

Construction of 2D radial models extracting reservoir properties

Total flow capacity distribution assigned to the 3D reservoir model and candidate locations for test wells

(mD-m)


locations for 2D radial models (β MH zone) Furthermore, as shown in Figure 16, the vicinity of the β-pt1 and β-pt3 locations were extracted from the 3D reservoir models to construct the 3D sector models having about 2,000,000 grid blocks. Although the β-pt3 location has the highest flow capacity among these 5 candidate locations, this location is quite far from the β-1 well and lies in the different channel body from that of the β-1 well. The reservoir properties assigned to this location in the 3D reservoir model may not be reliable. Therefore, in addition to the β-pt3 location, the location closest to the β-1 well (i.e., β-pt1 location) was also selected for the further investigation using a 3D sector model that can reflect the effects of lateral heterogeneity and reservoir dip.

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locations for 3D sector models (β MH zone) Prediction of test performances The test performances were predicted with 2D radial and 3D sector models. The case conditions set for these studies were the same as those specified in the study for the α MH concentrated zone. That is, in the base case, absolute permeability estimated from the porosity and permeability correlation, 3 MPa of bottomhole pressure and almost full completion interval (~50 m) were assumed. In addition, several case simulations were conducted to investigate the effects of the absolute permeability in sand layers, completion interval and bottomhole pressure (Table 4). The results of these simulation runs are summarized in Tables 6 and 7 for the radial model study and sector model study, respectively. 2D radial model study Six (6) case simulation runs, including the base case simulation, were executed as listed in Table 6. Figure 17 depicts the gas and water production for 30 days predicted for each candidate test well location as well as for the β-1 well in the base case. β-1 well showed the smallest gas and water production rates (approximately 20,000 m3/d and 250 m3/d, respectively) among the candidate test well locations, because the flow capacity, especially the thickness of MH concentrated interval around this well is estimated to be the smallest. In the case of the candidate test wells located in the region of higher flow capacity or thicker MH interval, the gas and water production was predicted to be 1.5-2.0 times higher than that of the β-1 well, which was almost proportional to the thickness of MH interval.

As summarized in Table 6, in all the candidate test well locations, the gas production was predicted to be about 80% of that in the base case, if the completion interval became shorter (about 70 % of the full completion). It was also predicted that the gas production became about 60% of that in the base case, if the bottomhole pressure was reduced only to 4 MPa. In the cases that the absolute permeability of sand layers is as high as 1,000 mD, the gas production was predicted to increase by the factor of 2.5-3.0 in comparison with the cases with the absolute permeability estimated from the porosity-permeability correlation.

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Figure 17 Base case production during test predicted by 2D radial models (β MH zone)

3D sector model study As mentioned in the above, the vicinities of the β-pt1 and β-pt3 locations were selected for the 3D sector model study (Figure 16). The β-pt1 is located closest to the β-1 well and β-pt3 showed highest gas producibilty in the radial model study. As summarized in Table 7, the gas and water production predicted in the sector model studies was very similar to that in the radial model study.

Figure 18 depict the oil and gas production for the β-pt3 location predicted in various cases. Figure 19 presents the cross sectional views of the distributions of pressure, temperature, MH saturation and gas saturation after 30 days from the commencement of the test, predicted in the base case for the β-pt3 location. As shown in this figure, the area of MH dissociation extends to 50-60 m from the well in some layers. Although the water flowed toward the test well from the obliquely downward water bearing zone, its effect on gas and water production were predicted not to be significant during 30 day production test. Since the small scale of heterogeneity such as small changes in reservoir properties may not dramatically affect the short term test performance, the results of the 3D sector model studies became similar to those of the radial models studies, without being affected by the large scale heterogeneity of formation dip.

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Figure 19 Cross sectional views of reservoir

properties at end of test predicted by 3D sector models for β-pt3 location

RECOMMENDATIONS ON SPECIFICATIONS OF PRODUCTION TEST The detailed specifications of the production test should be designed in a comprehensive manner, taking a variety of conditions such as operability, risks and outcomes into consideration. A part of the schemes of the production test are recommended in the following, referring to the results of the above simulation studies. Although these recommendations are made only from the production point of view, they must be beneficial toward the grand design of the production test. Location of test well From the production point of view, there are no significant differences between α and β MH concentrated zone. Since the α MH concentrated zone is composed of lobe type turbidite deposits, good continuity of reservoir properties is expected. That is, even at the location somewhat apart from the exploratory well (α-1 well), reservoir quality similar to that in the vicinity of the α-1 well may be expected. On the contrary, due to the same reason, dramatic increase in the gas production in comparison with that estimated for the α-1 well may not be expected. Even if the good continuity can be expected in the α MH concentrated zone, allocation of the test well outside of the fault block where the α-1 well exists may not be practical, with concern of the discontinuity of the reservoir properties across faults. It is also highly recommended to locate the test well as distant from faults as possible to reduce the effects of faults.

In the case of the β MH concentrated zone, it may be very important to locate the production test well in the channel containing MH sufficiently. In the channel type of turbidite deposits, it is very difficult to accurately estimate the distributions of properties in the different channel bodies. That is, the gas production rate in the production test may increase drastically in comparison with that predicted for the vicinity of the exploratory well (β-1 well). On the other hand, there is a risk that the gas production becomes quite small in different channels. Hence, it is recommended to allocate the test production well in the same channel body as that of the β-1 well where the deposit of MH was confirmed. Furthermore, the production test well should be located as close to the β-1 well as possible, taking account of the unexpected change in the reservoir properties even within the same channel body. Since the reservoir becomes up-dip in the south direction, the test well should be located in the south of the β-1 well, to suppress the water invasion to the well from the obliquely downward water bearing zone. Completion interval In the α MH concentrated zone, to increase the gas production during the test, the completion interval had better be as long as possible. It, however, should be determined taking account not only of the production but also of the bottomhole conditions to ensure the zone isolation and well integrity. On the other hand in the β MH concentrated zone, full completion over the MH concentrated interval may be dangerous depending on the well location and/or test duration, since it is suspected that the water flows into the well from the obliquely downward water bearing zone through layers located in the lower part of the reservoir. To control the water breakthrough at the test well, the completion interval should be restricted. Bottomhole pressure To increase the gas production, it is recommended to reduce the bottomhole pressure down to 3 MPa, if the conditions for installing downhole assemblies and a capacity of a electric pump allow. The bottomhole pressure is one of the key conditions to control the volume of MH dissociation and hence gas production.

Risks As predicted in the simulation studies, the absolute permeability of MH bearing layers (i.e., sand and silt layers) affects the gas production remarkably. If the absolute permeability is not as high as expected or decrease during the test due to damages such as fine migration and skin generation, the production rate may be much smaller than predicted. CONCLUSIONS The 3D reservoir models for vicinities of the α-1 and β-1 exploratory wells drilled in the MH concentrated zones in the Eastern Nankai Trough, were constructed integrating the results of the re-interpretation of seismic data, well log data and core analysis results. A series of numerical simulations were then conducted using 2D radial models and 3D sector models built by extracting from these 3D reservoir models, to predict performances of the first offshore production test assuming a variety of well locations and test schemes. Through the study targeting the α MH concentrated zone, the following are clarified.

• A 3D sector model can predict test performances more accurately than 2D radial model, reflecting the effects of various scales of heterogeneity such as faults and local change in reservoir properties.

• In the base case, the stable gas and water production rates during 30-day production test are predicted to range from ~15,000 to ~20,000 m3/d and from ~150 to ~350 m3/d respectively, depending on the test well location. The area of MH dissociation is estimated at about 30-40 m from the well in the lateral direction

• The predicted production rates should be different from the reality, if reservoir properties and operation conditions are different from those assumed in the prediction simulation. Especially, if absolute permeability of a test area is as high as 1,000 mD, gas production rate may be 3-4 times greater. On the other hand, gas production rate becomes far smaller in applying larger bottomhole pressure and/or shorter completion interval.

• Even if a well is located in a fault block

different from that of the exploratory well (α-1), the simulation suggested the good test performances of such a well. However, the selection of the different fault block as a test location may be dangerous because there are still many uncertainties in reservoir properties including the geological continuity across faults.

• Test well location should be determined based not only on the expected production but also on many factors such as operability conditions of sea bed and overburden/underburden, reliability of reservoir data, environmental aspect, etc.

Through the study targeting the β MH concentrated zone, the following are clarified.

• A 3D sector model can predict test performances more accurately than 2D radial model, reflecting the effects of various scales of heterogeneity such as formation dip, distribution of channel bodies and local change in reservoir properties.

• In the base case, the stable gas and water production rates during 30-day production test are predicted to range from ~20,000 to ~45,000 m3/d and from ~250 to ~650 m3/d respectively, depending on the test well location. The area of MH dissociation is estimated at about 50-60 m from the well in the lateral direction

• The predicted production rates should be different from the reality, if reservoir properties and operation conditions are different from those assumed in the prediction simulation. Especially, if absolute permeability of a test area is as high as 1,000 mD, gas production rate may be 2.5-3.0 times greater. On the other hand, gas production rate becomes far smaller in applying larger bottomhole pressure.

• Even if a well is located in a channel body different from that of the exploratory well (β-1), the simulation suggested the good test performances of such a well. However, the selection of the different channel body as a test location may be dangerous because there is no guarantee that the same quality of MH deposits exist in channel bodies different from that of the exploratory well.

• Test well location should be determined based

not only on the expected production but also on many factors such as operability, conditions of sea bed and overburden/underburden, reliability of reservoir data, environmental aspect, etc.

ACKNOWLEDGEMENT This study was financially supported by the Research Consortium for Methane Hydrate Resources in Japan (MH21 Research Consortium) to carry out Japan’s Methane Hydrate R&D Program by the Ministry of Economy, Trade and Industry (METI). The authors gratefully acknowledge them for the financial support and permission to present this paper. The authors also wish to thank Japan Oil Engineering Co. Ltd., Japan Oil, Gas and Metals National Corporation, the National Institute of Advanced Industrial Science and Technology, Schlumberger K. K. and the University of Tokyo for their technical support. REFERENCES [1] Kurihara M, Funatsu K, Ouchi H, Masuda Y, Narita H. Investigation on applicability of methane hydrate production methods to reservoirs with diverse characteristics. Proceedings of the 5th International Conference on Gas Hydrates, Trondheim, Norway, 2005. [2] Kurihara M, Sato A, Ouchi H, Narita H, Masuda Y, Saeki T, Fujii T. Prediction of gas productivity from Eastern Nankai Trough methane-hydrate reservoirs. SPE Reservoir Evaluation & Engineering, June 2009, 477–499. [3] Kurihara M, Sato A, Ouchi H, Narita H, Ebinuma T, Suzuki K, Masuda Y, Saeki T, Yamamoto K, Fujii T. Prediction of production test performances in Eastern Nankai Trough methane hydrate reservoirs using 3D reservoir model. Paper OTC 20737 presented at the Offshore Technology Conference, Houston, Texas, 2010. [4] Takahashi H, Yonezawa T, Takedomi Y. Exploration for natural hydrate in Nankai-Trough wells offshore Japan. Paper OTC 13040 presented at the Offshore Technology Conference, Houston, Texas, 2001. [5] Takahashi H, Tsuji Y. Multi-well exploration program in 2004 for natural hydrate in the Nankai-Trough offshore Japan. Paper OTC 17162 presented at the Offshore Technology Conference, Houston, Texas, 2005. [6] Saeki T, Fujii T, Inamori T, Kobayashi T, Hayashi M, Nagakubo S, Takano O. Delineation

of methane hydrate concentrated zone using 3D seismic data in the Eastern Nankai Trough. Proceedings of the 6th International Conference on Gas Hydrates, Vancouver, Canada, 2008. [7] Masuda Y, Naganawa S, Ando S, Sato K. Numerical calculation of gas-production performance from reservoirs containing natural gas hydrates. paper SPE 38291, Proceedings, Western Regional Meeting, Society of Petroleum Engineers, Long Beach, California, June 25-27, 1997. [8] Kurihara M, Ouchi H, Inoue T, Yonezawa T, Masuda Y, Dallimore SR, Collett TS. Analysis of the JAPEX/JNOC/GSC et al. Mallik 5L-38 gas

hydrate thermal production test through numerical simulation. Geological Survey of Canada, Bulletin 585, 2005. [9] Masuda Y, Konno Y, Iwama H, Kawamura T, Kurihara M, Ouchi H. Improvement of near wellbore permeability by methanol stimulation in a methane hydrate production well. paper OTC 19433, Proceedings of the Offshore Technology Conference, Houston, Texas, 2008. [10] Inamori T, Hato M, Suzuki K, Saeki T. Rock physics model in the unconsolidated methane hydrate bearing sediments. Journal of the Japanese Association of Petroleum Technology 2010;75(1): 59-71.

Average gas water

Gas Water Gas Water ratio (m3/m3)base 19,516 506 585,471 15,172 38.594MPa 6,895 299 206,852 8,960 23.09perf 14,855 252 445,662 7,566 58.90

1000mD-base 62,924 962 1,887,710 28,846 65.441000mD-4MPa 20,704 516 621,115 15,486 40.111000mD-perf 47,992 566 1,439,760 16,992 84.73

base 13,994 140 419,818 4,210 99.724MPa 4,540 68 136,204 2,043 66.67perf 9,819 100 294,566 3,002 98.12


base 13,837 162 415,099 4,852 85.554MPa 3,861 83 115,827 2,491 46.50perf 9,870 134 296,085 4,022 73.62


base 18,822 320 564,666 9,606 58.784MPa 6,198 184 185,936 5,533 33.60perf 11,587 175 347,620 5,249 66.23


base 12,962 176 388,852 5,289 73.524MPa 3,457 86 103,699 2,571 40.33perf 10,126 113 303,780 3,381 89.85


base 12,991 186 389,739 5,588 69.754MPa 3,479 93 104,366 2,787 37.45perf 9,595 109 287,862 3,280 87.76


Average production rate (m3/d) Cumulatiove production (m3)Location Case

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α-pt1

α-pt2

α-pt3

α-pt4

α-pt5

Table 2. Summary of 2D radial model study results for α MH concentrated zone

Average gas water

Gas Water Gas Water ratio (m3/m3)base 15,394 139 461,825 4,176 110.594MPa 4,575 64 137,244 1,907 71.97perf 11,037 99 331,109 2,970 111.48


base 19,289 353 578,661 10,576 54.714MPa 5,917 198 177,510 5,933 29.92perf 11,882 195 356,473 5,849 60.95


α-pt1

α-pt3

Average production rate (m3/d) Cumulatiove production (m3)Location Case

Table 3. Summary of 3D sector model study results for α MH concentrated zone

Average gas waterGas Water Gas Water ratio (m3/m3)

base 19,418 260 582,525 7,790 74.784MPa 12,047 203 361,413 6,076 59.48perf 5,371 38 161,139 1,129 142.73

1000mD-base 45,817 339 1,374,497 10,156 135.341000mD-4MPa 26,388 239 791,636 7,159 110.581000mD-perf 14,545 146 436,350 4,390 99.40

base 35,910 489 1,077,300 14,655 73.514MPa 20,109 341 603,259 10,239 58.92perf 22,220 293 666,603 8,793 75.81

1000mD-base 89,005 709 2,670,136 21,257 125.611000mD-4MPa 57,690 539 1,730,689 16,162 107.081000mD-perf 49,115 371 1,473,443 11,144 132.22

base 43,125 599 1,293,747 17,980 71.954MPa 24,042 417 721,247 12,519 57.61perf 31,165 401 934,947 12,026 77.74


base 44,608 614 1,338,227 18,429 72.624MPa 22,844 400 685,313 12,011 57.06perf 32,072 457 962,160 13,696 70.25

1000mD-base 127,473 1,032 3,824,179 30,960 123.521000mD-4MPa 72,072 707 2,162,150 21,221 101.891000mD-perf 88,051 751 2,641,521 22,520 117.30

base 36,457 533 1,093,720 16,003 68.344MPa 16,734 322 502,007 9,651 52.02perf 31,178 452 935,353 13,563 68.96


base 25,281 402 758,440 12,059 62.894MPa 12,974 268 389,210 8,039 48.42perf 23,448 365 703,453 10,959 64.19


Case Average production rate (m3/d) Cumulatiove production (m3)

β-1

β-pt1

Location

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β-pt3

β-pt4

β-pt5

Table 6. Summary of 2D radial model study results for β MH concentrated zone

Average gas water

Gas Water Gas Water ratio (m3/m3)base 37,737 590 1,132,119 17,689 64.004MPa 21,801 402 654,043 12,045 54.30perf 25,795 305 773,847 9,143 84.64

1000mD-base 84,747 1,238 2,542,411 37,139 68.461000mD-4MPa 55,277 1,003 1,658,321 30,094 55.111000mD-perf 55,328 411 1,659,846 12,319 134.74

base 36,999 555 1,109,984 16,645 66.694MPa 24,195 391 725,855 11,716 61.95perf 34,188 460 1,025,625 13,803 74.31


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Table 7. Summary of 3D sector model study results for β MH concentrated zone

prediction of performances of methane hydrate production tests in

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