Three-dimensional far field reservoir characterization from deep azimuthal electromagnetic, seismic, and offset well data

Abstract

Deep azimuthal electromagnetic ("DAEM") data, is combined with seismic data and resistivity data from an offset well to generate a saturation model solution of a reservoir in hydrocarbon bearing subterranean formation. The DAEM data is inverted to create a stratigraphic framework, which is updated with rock types to form an updated framework. The updated framework is combined with the DAEM data to obtain the saturation solution model. Hydrocarbons in the reservoir are identified and produced based on the model.

Description

Attorney Docket No.: 65REL-510197-WO-2 (000236) THREE-DIMENSIONAL FAR FIELD RESERVOIR CHARACTERIZATION FROM DEEP AZIMUTHAL ELECTROMAGNETIC, SEISMIC, AND OFFSET WELL DATA INVENTOR(S): Warren Fernandes (DE), Tim Salter (UK), Santi Randazzo (US), Bhawesh Jha (UAE), Sergey Martakov (US), Sanathoi Potshangbam (UAE), David John Holbrough (UK), and Simon Austin (UK) CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of co-pending U.S. Provisional Application Serial No. 63 / 632,754, filed April 11, 2024, the entirety of which is incorporated by reference herein in its entirety and for all purposes. BACKGROUND OF THE INVENTION 1. Field of Invention

[0002] The present disclosure relates to reservoir characterization, and more specifically is directed to characterizing a reservoir by combining deep azimuthal electromagnetic, surface seismic, and offset well data. 2. Description of Prior Art

[0003] Hydrocarbons produced from within a subterranean formation typically flow to the surface inside a wellbore that extends from the surface into the formation. Most wellbores are formed with drilling assemblies of a drill string rotated by a top drive or rotary table on the surface. Drill strings typically include lengths of tubulars joined together in series, and a drill bit attached to a lower end of the series of tubulars. Downhole testing tools are lowered into the wellbore to measure a plurality of subsurface properties in the subterranean formation surrounding the wellbore. The characterization of subsurface properties in the subterranean formation is useful in gauging the producibility of hydrocarbon bearing formations for reserve assessment and production optimization. These subsurface properties may be integrated with IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) multi-physics core measurements across different plug scales and log scales to provide constraints on reservoir model building for hydrocarbon saturation models. SUMMARY OF THE INVENTION

[0004] In some embodiments, a hydrocarbon saturation model within reservoir formations is determined based largely on the integration of well data, which is typically limited to less than one foot depth of investigation around the wellbore. The well data then has to be extrapolated into a much larger stratigraphic framework volume across part or all of the investigated area. The significant gap in spatial resolution between the conventional well data and the seismic data leads to uncertainty in the estimation of reservoir saturation between wells, particularly in the propagation of log-scale properties away from the well. Statistical analysis of suitably screened offset wells provides an understanding of the likely proportion of different reservoir rock types and saturations that may be expected within any individual reservoir formation. Reservoir properties estimated only from seismic impedance (or other volumetric attributes) may usefully define spatial reservoir trends, but the seismic products can have deficiencies related to seismic noise and uncertainties in the seismic inversion workflows themselves. The influence of uncertainties in estimating reservoir saturation between wells may be reduced by using resistivity derivatives measured from the deep azimuthal electromagnetic tools. The deep azimuthal resistivity derivatives provide the ability to combine seismic and log data together and so aid reservoir characterization in and around the near to far field of a suitably logged well. Thus, a relationship between reservoir rock type, height to hydrocarbon fluid contacts, and hydrocarbon saturation is determined to accurately characterize saturation in distal regions of a formation surrounding a wellbore. Rock-fabric and relative height above a hydrocarbon- water contact in the hydrocarbon saturation model may be used to determine a reliable saturation distribution of hydrocarbons in a reservoir. As described in more detail below, in the IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) hydrocarbon saturation model, a spatial rock-type model of the reservoir is developed based on offset wells or analogues, and then the resistivity solution for any remotely sensed reservoir unit is transformed into a reservoir saturation value. For the rock-type model, the hydrocarbon saturation of rock positioned within a structure containing hydrocarbons (oil or gas) is controlled by the microscopic pore system within that rock. Thus, the water saturation is useful to determine the hydrocarbon saturation for a particular hydrocarbon reservoir. The pore system varies through different parts of the reservoir based on a plurality of reservoir properties of the original sediments, such as grain size, sorting, compaction, cementation, etc. This variation is described in a rock-type proxy model, in which parts of the reservoir are assigned to one of several discrete rock types qualifying it with another continuous rock characteristic (such as porosity) or using both discrete and continuous descriptors together. The rock type is typically described for the reservoir-prone units, with the non-reservoir-prone units defined as non-permeable with no potential hydrocarbon saturation. BRIEF DESCRIPTION OF DRAWINGS

[0005] Some of the features and benefits of the present invention having been stated, others will become apparent as the description proceeds when taken in conjunction with the accompanying drawings, in which:

[0006] FIG. 1A is a side sectional view of an example of forming a wellbore through a subterranean formation using a drilling system with a deep azimuthal electromagnetic measurement (“DAEM”) tool.

[0007] FIG.1B is a graph of information obtained with the DAEM tool.

[0008] FIG.2A is a side sectional view of an example of a surface seismic acquisition system.

[0009] FIG.2B is a graph of information obtained by the surface seismic acquisition system. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236)

[0010] FIG. 3 is a graphical example of the results of resistivity inversion output from the DAEM tool of FIG.1A showing detected bed boundaries extending far around a wellbore.

[0011] FIGS. 4-7 are graphic examples of stages of populating a three-dimensional grid with information imaged from the formation of FIG.1B.

[0012] FIG. 8 is a graph having plots correlating near-wellbore resistivity data from offset wells that sample the same framework of differing geologic formations as the wellbore in which DAEM data is collected.

[0013] FIGS.9 and 10 are graphical examples of a workflow for generating resistivity values from a seismic derived impedance volume.

[0014] FIG.11 is a three-dimensional representation of the formation of FIG.1 populated with resistivity information.

[0015] FIG.12 is an example plot of derived impedance measured in the formation of FIG.1.

[0016] FIG.13 is a three-dimensional representation of a saturation model of the formation of FIG.1.

[0017] FIG.14 is a flow chart of an example method for determining a saturation model of the formation of FIG.1.

[0018] FIG. 15A is a flow chart of an example method for building a stratigraphic framework for determining the saturation model of the formation of FIG.1.

[0019] FIG. 15B is a flow chart of an example method for building a well based rock model for determining the saturation model of the formation of FIG.1. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236)

[0020] FIG.15C is a flow chart of an example method for implementing a DAEM integration for determining the saturation model of the formation of FIG.1.

[0021] FIG. 15D is a flow chart of an example method for applying a geophysical validation for determining the saturation model of the formation of FIG.1.

[0022] While subject matter is described in connection with embodiments disclosed herein, it will be understood that the scope of the present disclosure is not limited to any particular embodiment. On the contrary, it is intended to cover all alternatives, modifications, and equivalents thereof. DETAILED DESCRIPTION OF INVENTION

[0023] The method and system of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings in which embodiments are shown. The method and system of the present disclosure may be in many different forms and should not be construed as limited to the illustrated embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey its scope to those skilled in the art. Like numbers refer to like elements throughout. In an embodiment, usage of the term “about” includes + / - 5% of a cited magnitude. In an embodiment, the term “substantially” includes + / - 5% of a cited magnitude, comparison, or description. In an embodiment, usage of the term “generally” includes + / - 10% of a cited magnitude.

[0024] It is to be further understood that the scope of the present disclosure is not limited to the exact details of construction, operation, exact materials, or embodiments shown and described, as modifications and equivalents will be apparent to one skilled in the art. In the drawings and specification, there have been disclosed illustrative embodiments and, although IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) specific terms are employed, they are used in a generic and descriptive sense only and not for the purpose of limitation.

[0025] In some embodiments, a downhole testing tool includes a probe assembly configured with a plurality of sensors disposed adjacent to a port of the probe assembly to measure a plurality of petrophysical properties of subterranean formations. The plurality of sensors are used to irradiate the subterranean formation near the wellbore with energy, such as, in the form of one or more electromagnetic, seismic, or acoustic waves. Monitoring the reflection of these waves provides stratigraphic information about the subterranean formation, such as the location and contour of stratigraphic boundaries. For example, a stratigraphic boundary is a subterranean bed boundary which is a surface / interface between two different subterranean formations. As another example, when a plurality of subterranean formations are stacked vertically the stratigraphic boundaries are the sub-horizontal surfaces between them. The stratigraphic boundaries are useful for identifying the presence of hydrocarbons trapped in the subterranean formation. When a particular subterranean formation contains hydrocarbon fluids it is often referred to as a reservoir formation.

[0026] Traditionally, a hydrocarbon saturation model within a plurality of prospective or proven reservoir formations is generated within a stratigraphic framework at least based on an integration of well data which is limited to one or two decimeters around the wellbore. The stratigraphic framework is generally used for describing the succession of differing aged geological formations, that may be recognized by slightly differing overall geometry, or proportion of different pore-scale fabrics (known as rock types). The stratigraphic framework will typically capture formations that are reservoir-prone or non-reservoir-prone. For example, a subterranean formation includes reservoir-prone source rocks which are permeable to allow IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) hydrocarbon accumulation. As another example, a subterranean formation includes non- reservoir prone source rocks which lack in permeability and form hydraulic seals.

[0027] There is often a significant gap in spatial resolution between the conventional well data that describes the internal detail of formations at an individual well and the seismic data that describes the large-scale architecture of the formation bounding horizons. This significant gap leads to uncertainty when tying the conventional well data and the seismic data for the same subterranean formation, particularly during the propagation of log-scale properties away from the well. Thus, the hydrocarbon saturation model has significant volumetric uncertainties in the estimations of reservoir saturation between wells using the conventional well data and the seismic data. However, the accuracy of the hydrocarbon saturation model may be further improved by using deep azimuthal electromagnetic measurements (“DAEM”). In some embodiments, the DAEM acquired during drilling may extend the acquisition of measured data significantly further in both lateral and vertical directions from the wellbore compared with all other logging while drilling (“LWD”) and wireline measurements. For example, DAEM refers to LWD multi-propagation resistivity measurements associated with azimuthal components. The DAEM measurements can be used in an inversion process to estimate reservoir saturation of the subsurface structure ranging from a few inches up to a few hundred feet away from a borehole in which the DAEM mapping is being performed. Essentially the data gap between wells is reduced by the population of several hundred feet of electromagnetic data. Combining DAEM, surface seismic, and offset well data increases the accuracy of the hydrocarbon saturation model. An example of seismic imaging or exploration includes sending energy waves or sound waves into the earth and recording the wave reflections to indicate the type, size, shape, and depth of a subsurface rock formation. Seismic imaging provides structural and static information for a reservoir, such as the lateral extent of the reservoir, thickness, faults, and porosity, among other reservoir properties. Offset well data is obtained from offset well IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) logs, core, and well test data, which is an existing wellbore proximate wellbore being imaged that may be used as a guide for planning and drilling a well. Multiple nearby offset wells proximate to a planned wellbore provide information that reduces uncertainty in that area. Examples of offset well data include gamma ray (API unit), resistivity (ohm.m), neutron porosity (Pu), density (g / cc), surveys (measure depth, inclination, and azimuth) for each offset well, formation tops, etc. An example of a saturation model is the functional relationship between saturation, rock quality, and height above the free-water level of any hydrocarbonaccumulation, and a saturation solution is the predicted water saturation ( ) at any point inthe reservoir that results from application of a saturation model.

[0028] In some embodiments, timestamps of when the DAEM, seismic, and offset well data are acquired provide a time-lapse distribution of the movement of the subsurface fluids. An advantage of more accurate saturation models is that they improve field lifecycle economics by (1) improving recovery through the identification of infill targets; (2) reducing field development costs by identifying opportunities for reduction of the number of planned wells; (3) improving production and recovery through an optimized completion design that equalizes inflow control and minimizes early water break through; and (4) enabling sweep efficiency monitoring.

[0029] In some embodiments, the depth of detection (“DOD”) of the LWD tools is configured to vary azimuthally around a wellbore being electromagnetically mapped. For example, a deep azimuthal resistivity (“DAR”) tool has a DOD of up to around 30 feet. As another example, an extra deep azimuthal resistivity (“EDAR”) tool has a DOD ranging between around 100- 150 feet. As another example, an ultra-deep azimuthal resistivity (“UDAR”) tool has a DOD of up to around 300 feet. In examples and for the purposes of discussion herein, data obtained from the DAR, EDAR, and UDAR tools is referred to as deep azimuthal electromagnetic (“DAEM”) IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) data. In non-limiting examples, the DAEM data from an LWD tool is used to invert the formation resistivity radially around the wellbore within a DOD of the LWD tool. The rock volume within the DOD of the LWD tool is also optionally referred to as the “halo” of electromagnetic data, which in embodiments bridges the gap between normal well log measurements taken from within the wellbore being mapped and surface acquired seismic data. In alternatives, the DAEM data includes all information acquired from DAR, EDAR, and UDAR LWD tools. As a result, a stratigraphic framework may be generated based on an integration of well log measurements, DAEM data, one or more local geological models, and seismic information. Within the stratigraphic framework, a physics-based solution is obtained to estimate saturation away from the wellbore. The stratigraphic framework is implemented to improve the ability to extend the depth of petrophysical reservoir characterization to obtain the halo of electromagnetic data. In particular, the stratigraphic framework provides several advantages: (1) higher confidence of the stratigraphic framework with detailed reservoir and non-reservoir sub-zones in the DOD halo of electromagnetic data around the subject wellbore; (2) increased accuracy of a lateral extension of the stratigraphic framework constructed from a combination of DAEM data detected bed-boundaries and seismic interpretation; (3) a mutual validation and update of the existing seismic time-depth domain conversion in the vicinity of the wellbore; (4) options for using acoustic impedance deep electromagnetic (“AIDEM’) rock types or non-seismic analogue rock-types to guide the transformation of resistivity into saturation within remotely sensed reservoir units; (5) more precise saturation estimate within the halo around the subject wellbore; (6) options for rock type and saturation relationship development and propagation outside the DOD of deep azimuthal electromagnetic data (with or without seismic data); (7) synergy with existing reservoir navigation procedure to provide real-time updates of reservoir characterization; and (8) utilization of artificial intelligence / machine learning techniques for further improvement of saturation model estimates. In a IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) further explanation, the stratigraphic framework is the succession of rock units that were deposited periodically and can now be distinguished from bulk character. Different units / formations of rock are bound by sub-horizontal, flat to gently dipping structural surfaces. The stratigraphic framework is typically recognized from one well based on one or two available formation evaluation logs. However, in the present disclosure, the stratigraphic framework is recognizable from other wells using other logs and in particular by deep penetrating DAEM data. The hydrocarbon saturation of an otherwise water saturated subsurface rock unit is dependent upon the continuity and size of its pore fabric. For example, a rock-fabric or equivalently a rock-type proxy is used to predict the saturation of a corresponding subsurface rock unit. The rock-fabric proxy includes a discrete classification or a continuous property, such as porosity or permeability. Offset wells (not shown), in addition to offset well 15, are optionally available in the vicinity of an area of interest (AOI) that provides partial or complete coverage of a study stratigraphic interval. As another example, one or two of the offset wells (including offset well 15) are considered to be the most representative well (“key offset well”) for proposing a stratigraphic framework due to a plurality of lateral changes in rock character and depth.

[0030] Shown in a side sectional view in FIG. 1A is an example of logging while drilling in which a drill string 10 is forming a wellbore 12 in formation 14, and in which the wellbore 12 is deviated. An example of an offset well 15 is shown spaced away from wellbore 12 and in the same formation 14. In the example shown, a plurality of DAEM data sets are obtained by irradiating the formation 14 with electromagnetic energy 16 to map the formation 14 to obtain information about bed boundaries 181-n (e.g., boundary 181-n contours and distances from the wellbore 12), and deep reading resistivity in a halo of investigation up to 300 ft radially from the wellbore 12. The string 10 includes a drill bit 20 and an LWD tool 22 for mapping. Depths of the boundaries 181-n are estimated with knowledge of distance from the wellbore 12. For IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) example, the depths of the boundaries 181-n are utilized to update a pre-drill (well or seismic- based) structural model, such as a saturation model, based on well log measurements or seismic information. In an example, a saturation solution is developed for the halo of investigation based on resistivity measurements from the mapping. Graphically illustrated in FIG. 1B is an example of azimuthal measurements obtained with the LWD tool 22, which have been inverted to provide an estimate of formation resistivity around the wellbore 12. An advantage of the disclosed method based on an integration of the well log measurements, the plurality of DAEM data sets, the pre-drill structural model, and the seismic information is that the structure and resistivity predictions are extrapolated, with increased confidence of accuracy, into a larger rock volume beyond the DAEM halo to improve existing three dimensional (“3D”) geo- models.

[0031] Referring now to FIG. 2A, shown in a side partial sectional view is an example of obtaining seismic data, in which a seismic source 24 is shown mounted onto a surface truck 26. In the example, source 24 generates acoustic waves 28 shown propagating into the formation 14. The waves 28 reflect from interfaces within the formation 14, such as the boundaries 181-n, to produce reflected waves 32, which propagate back to the surface where they are sensed and monitored by geophones 30. Graphically shown in FIG.2B is an example of seismic data obtained by monitoring the reflected waves 32. For example, the seismic data provides information about the type, size, shape, and depth of the rock structure within formation 14, including that of the boundaries 181-n. Seismic data also optionally includes information for reservoir 32 shown within formation 14, such as the structural and static information of reservoir 32, its lateral extent, thickness, faults, porosity, and the like. In different applications, the DOD of the LWD tool 22 (referring to FIG. 1A) is variable due to the conductivity of the rocks and fluid surrounding the well, which adds a significant volume of investigation. Alternatives exist in which seismic data is included for an enhancement with IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) data from the DAEM study well and a suitable offset well, and in other alternatives, seismic data is not included.

[0032] Further in this example, a three-dimensional far-field saturation solution (“saturation solution”) is generated by combining DAEM data, such as that obtained using LWD tool 22 and illustrated in FIG. 1B, with seismic information, such as that shown in FIG. 2B. The saturation solution is obtainable over a wide range of data scenarios, e.g., seismic rich, seismic poor, DAEM rich, and DAEM poor. In a non-limiting example of operation, the saturation solution is derived in three steps: (1) building a stratigraphic framework of structural surfaces and their reservoir and non-reservoir zones; (2) confirming and populating the rock-type classes from offset wells (or extracted from log-seismic relationships); and (3) integrating DAEM surface depth and resistivity information into the three-dimensional framework. In alternatives, the offset wells include those deemed as key, and which are suitable to capture the range of resistivity responses in the modeled volume. In some embodiments, a reservoir saturation solution is developed within the “halo” of DAEM investigation around wellbore 12. In some embodiments, the saturation solution is generated based on well data only or a process based upon well data and three-dimensional seismic volumes.

[0033] The Stratigraphic Framework

[0034] In a non-limiting example, the DAEM data is inverted to identify resistivity defined bed boundaries 181-n, and their respective depths measured radially from the wellbore 12. When the depth of detection range of the DAEM tool reaches 30-300 ft, complications arise in providing multiple boundary solutions by visual analysis of the DAEM data. To resolve this challenge, an inversion application which is an advanced mathematical algorithm is used to interpret the DAEM data. The resistivity distribution in geological formations is selectively approximated locally by resistivity models of different complexity. In this example, IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) homogeneous formation is represented by a simple model with one resistivity value (zero- dimensional model) that does not change in any direction, and layered formations are represented by a one-dimensional model with layers having different resistivities and thicknesses, with resistivity changing in a single direction – true vertical (or stratigraphic) depth (“TVD”). In some embodiments, generalization includes two-dimensional and three- dimensional models where resistivity would be changing in two or three directions, respectively. For a given model, the predicted responses of the DAEM tool are alternatively calculated based on Maxwell’s equations (forward modeling). When this process is reverted, it is called the resistivity inversion, i.e., the tool measurements become a known input and the objective is to find a resistivity model where tool responses would match the measured ones. The inversion calculates an earth model where there are many unknown parameters, vastly increasing for higher dimensionality. The earth model is determined by a series of calculations (thousands or millions of iterations) which use a curve fit between measured and synthetic data as the main criterion for achieving the solution. An example of the inversion method includes supplying an initial expected earth model based on the information from the nearby offset wells, with the inversion algorithm changing the model parameters until a result with the best curve fit is achieved. Further in this example, the inversion algorithm is applied multiple times for a series of data intervals along the well to produce a series of local models of appropriate dimensionality. Eventually, the local models are combined into a single three-dimensional model around the wellbore 14 to describe resistivity distribution inside the sensitivity range of the DAEM tool. The locations and contours of the boundaries 181-n are correlated with established reservoir surfaces in offset / type wells to define a working stratigraphic model applicable to the halo around the wellbore 12. The stratigraphic model is optionally hierarchical, with both major (e.g., seismic) and minor (e.g., well-log-based) boundaries defined. The stratigraphic boundaries defined from DAEM data are represented by elongated IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) depth ribbons 34 (FIG.3) shown projecting above and below wellbore 12. In this example, the ribbons 34 are combined or merged with existing field-wide reservoir depth surfaces to then build a depth model that encompasses the halo of rock detected by the DAEM data plus its lateral continuation into the surrounding reservoir volume and its tie with suitable offset wells. For example, existing ‘regional’ surfaces have existed to allow the well to be drilled with suitable targets. The process described revises the regional surfaces in the area around the well by replacing and merging them with the depth ribbons to provide an updated depth surface. In examples in which three-dimensional seismic information is available proximate to the wellbore 12, the stratigraphic model is optionally improved by matching spatial information about the bed boundaries 181-n from the DAEM mapping with that from the seismic survey, and adjusting any existing time-depth relationships to obtain the required matching of the two data, both within and away from the DAEM halo. In an embodiment, seismic data is obtained before drilling and is recorded in time, which is later converted to depth using a velocity model. Optionally, DAEM is recorded during or post drilling in depth and with better resolution than the seismic data. In these examples, DAEM data is given greater weight and used to improve the velocity model to convert seismic from time to depth domain, which in embodiments affects the stratigraphy definition in regions away from the DAEM halo. The position of a surface recorded in time through seismic and recorded in depth through DAEM data is alternatively used for adjusting the time depth relationship. The stratigraphic framework described is optionally built either before the drilling of the study well (and then updated in real-time) or built in a post-well environment once all data has been collated. In examples in which the stratigraphic framework is built before the study well, existing regional surfaces are used; and that optionally incorporate DAEM data from other wells if that were available.

[0035] In an alternate embodiment, the structural framework is transformed into a suitably scaled three-dimensional geo-cellular grid or a voxel grid. Example grids include both the IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) DAEM near wellbore halo, which alternatively extends to a maximum DOD, and a broader volume that matches part of an existing field-wide three-dimensional geological grid. In some embodiments, the grid resolution is greater than what is normally employed for field-wide three-dimensional models that typically have a lateral cell size of 5 to 10 meters. An example of a three-dimensional stratigraphic compliant grid 36 generated as described above is graphically illustrated in FIG. 4. In alternatives, grid 36 provides a tool into which differing subsurface data sets are sampled, co-located, and repopulated.

[0036] Rock Type Classification

[0037] Rock type classification is performed in a subsequent step of generating the saturation solution, in this example rock type classification includes distributing a rock-fabric property through the three-dimensional grid space, which is to guide the saturation solution. The rock fabric is alternatively characterized based on effective porosities of the different stratigraphic units, which in embodiments is a rock type index identified from saturation and porosity (or their combination as a bulk-volume water (“BVW”) measure) trends in log or core data. Embodiments exist in which a rock type solution includes two to three reservoir rock types along with a non-reservoir class.

[0038] In an example in which no seismic data is available, rock type classification, or property modeling, the three-dimensional grid is populated with porosity information from offset wells (not shown). An example of a three-dimensional grid 36A populated with this porosity information is graphically shown in FIG.5. In alternatives, established geostatistical techniques per reservoir zone are used to generate the three-dimensional grid 36A. The rock-type index is then likewise populated into the three-dimensional grid 36A using relationships to each reservoir zone defined from offset wells or analogues, plus any other identified geological trends. In examples in which seismic data is included or otherwise available, the seismic IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) acoustic impedance (“AI”) is directly transformed to a suite of equivalent rock types, which guides the population of reservoir parameters in the three-dimensional grid 36B (FIG. 6). Geostatistical techniques for the 3D population of discrete reservoir rock-types or continuous bulk properties are guided through co-located or co-kriging by the seismic rock-types.

[0039] Seismic Inversion (P-Impedance) Resampled into the Common Three-Dimensional Grid Volume

[0040] In some embodiments, log domain data is used to establish a correlation of well-log resistivity to acoustic impedance across a suite of rock-physics identified rock types, which for discussion herein are referred to here as Acoustic Impedance Deep Electromagnetic (“AIDEM”) rock types. The AIDEM rock types are mapped into a three-dimensional volume 36C (FIG. 7) based on seismic impedance. Furthermore, resistivity logs from offset wells are standardized using the resistivity of selected shale beds. An example of selected shale beds are regional shale bed in which resistivities are expected to be generally consistent spatially (i.e., within a range of + / - 10% of resistivities values). Normalization assumes whatever changes are being observed are due to issues in tool measurement which are expected to be minimal. Dataanalysis is then applied to generate the relational parameters ( , and , factors) thatdescribe impedance vs resistivity across corresponding facies at a depth in a zone of study, as provided in Equation 1 below:where is the depth; is the impedance at the depth ; is the resistivity at the depth ;is a calibration factor; , and , are the relational parameters, and is the indexnumber of facies. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236)

[0041] Normalizing and calibrating the well data improves the accuracy of the correlation between the well data to the seismic data within the zone of study. This process leads to a well log domain acoustic characterization of a study volume linking the expected broader three- dimensional seismic response to the well data (FIG.8) from individual wells. Shown in FIG.9 is an example of a workflow to estimate the relations between rock types, acoustic impedance, and resistivity. In this example, the relationship, established at well log scale, is then applied to seismic inversion results to generate an initial seismic resistivity model that is then upgraded to a higher vertical resolution using the resistivity inversion algorithms of the DAEM. In some embodiments, this relation is applied in a reverse option to obtain an impedance model from the resistivity inversion solution and generate an impedance volume that is used to compare to the seismic data.

[0042] In some embodiments, the process described herein for obtaining a saturation solution is enhanced by the availability of seismic inversion volumes, which provide a three- dimensional spatial characterization of the study reservoir volume with the seismic inversion products including a direct facies (or rock-type) prediction. Lithofacies 106 (FIG. 10) are optionally generated by combining an elastic inversion result 102 from the seismic inversion volumes and well based facies 104 all in a common depth domain of the three-dimensional grid as graphically depicted in FIG.10. Thus, the lithofacies 106 can be used to generate the initial seismic resistivity model 108 from rock properties. An example of a seismic inversion volume is the elastic inversion process of using seismic data to obtain p-impedance, s-impedance, and density for lithology identification.

[0043] In some embodiments, single or multiple facies predictions from elastic inversion results are loaded into the three-dimensional grid volume 36D (FIG. 11), each providing slightly different facies probabilities and associated resistivity mapping. In the example of FIG. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) 11, seismic facies 38 are included in the three-dimensional grid volume 36D (four facies added into the grid cells), in embodiments each of these facies 38 has its own A and B factors for resistivity generation. To reiterate, seismic data is used in the following steps: (1) using wells and seismic inversion methods to generate the three-dimensional facies cube; (2) using initial results from resistivity to build a relation between rock types and resistivity; and (3) upgrading the three-dimensional model to be used in the final resistivity solution.

[0044] In some embodiments, an advantageous quality control step is to directly convert the DAEM resistivity output to determine impedance and a reflection series 42 in a synthetic seismogram 40 (FIG. 12) by using the relationship of impedance and resistivity. The example of FIG. 12 depicts well derived impedance converted into reflectivity and convolved with a seismic wavelet to generate the reflection series 42 in the synthetic seismogram 40, and that includes a comparison back to the measured seismic 44 within the DAEM detection halo.

[0045] Three-Dimensional Saturation Model Solution

[0046] In some embodiments, in the stage of developing the saturation solution, formation resistivity (Rt) interpretation from DAEM data is initially re-sampled into the three- dimensional reservoir grid 36E (FIG.13) as a near vertical 2D section along the study wellbore. The resistivity is then mapped into the three-dimensional grid, away from the study well. The stratigraphic framework is used for vertical control, and lateral variability measures extracted from the high spatial density of resistivity data itself may be used for lateral control. For example, when seismic volumes are available, the seismic information is used to provide another constraint on the lateral variability of the property distribution. The geostatistical techniques for the three-dimensional population of discrete reservoir rock types or continuous bulk properties are influenced by co-located or co-kriging to the seismic rock types. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236)

[0047] In some embodiments, the associated saturation of each cell is predicted after the grid contains a three-dimensional population of both the rock fabric (the porosity and / or rock-type index) and resistivity properties. The saturation solution may be updated in an iterative process which includes: (1) determining an initial saturation estimate from the resistivity, porosity, and rock-type values sampled at each cell of each zone within the high confidence DAEM halo; and (2) identifying and extrapolating possible hydrocarbon water contacts in individual reservoir zones in this initial solution. In some embodiments, fluid type and height above properties are generated to quality control the initial saturation solution to values expected for the transition zone or irreducible saturation zone. Furthermore, the updated saturation solution is propagated away from the study well halo into the larger three-dimensional volume.

[0048] Furthermore, quality control of each saturation prediction is followed by a back check of the three-dimensional grid solution, examples of which include: (1) establishing average saturation values within individual modeled zones and relating these to suitable offset wells, (2) comparing initial and current water saturation values (possibly defining zones of swept or un-swept hydrocarbons).

[0049] In some embodiments, the advantages of the disclosed approach include significant improvement in spatial resolution between the conventional well data and the seismic data. Reservoir properties estimated only from seismic impedance (or other volumetric attributes) may usefully define spatial reservoir trends, but the seismic products can have deficiencies related to seismic noise and uncertainties in the extent (a few inches only). Therefore, the addition of azimuthal resistivity derivates from a DAEM tool provides a log response deeper into the formation. This aids reservoir characterization around and in the near field of a suitably logged well with much improved confidence. From a commercial standpoint, the DAEM tool applies equally to Green fields as well as Mature Brown Fields which effectively improves IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) field lifecycle economics (especially relevant in mature brownfields) by (1) improving recovery by identification of infill targets, (2) reducing field development costs by identifying opportunities for reduction of the number of planned wells, (3) improving production and recovery by enabling optimized completion design to promote more equalized inflow control and minimize early water breakthrough, and (4) enabling monitoring of sweep efficiency when relating the data related to acquisition time stamps. Furthermore, the DAEM tool optionally incorporates other elements to provide a better saturation solution by adding the seismic inversion and other attributes to help extend and improve the lateral and vertical solutions. In the alternative, the DAEM tool generates a synthetic seismic and compares it to the real one to improve the seismic image.

[0050] In some embodiments, the DAEM well date is combined with seismic information to achieve a fully integrated three-dimensional saturation solution. Thus, each dataset is mutually beneficial to the other by providing spatial quality control (“QC”). An example of spatial QC involves the calibration of the (surface acquired) seismic and (wellbore acquired) DAEM information at differing points within the DAEM depth of investigation volume to establish the best relationship between the bulk rock parameters that are defined separately by the two (seismic and DAEM techniques), which is optionally done by cross-correlation. The three- dimensional saturation solution provides the advantage that bed boundaries detected by DAEM allow correction of the assumed time-depth relationships adopted by pre-drill seismic. Resistivity AI relationships help categorize the working rock-fabric model. Spatial trends and data variography identified in the seismic volume help to populate the rock-fabric model away from the study well both within and beyond the DAEM DOD.

[0051] In some embodiments, structural surfaces from the DAEM dataset build a stratigraphic framework in three dimensions around the well that will describe the architecture of the IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) reservoir-prone rock units for facilitating a saturation prediction of the reservoir-prone units that need to be populated with a rock-quality (rock-type) property. Examples of this include using traditional Gaussian property population techniques constrained with a variogram of vertical and lateral data similarity. In alternatives without seismic data, offset wells are used to define the range / type and trend plus variogram dimensions of rock-fabric reservoir properties for each zone, and then the geostatistical population of the reservoir property proceeds. The DAEM resistivity is screened, identifying those zones in which free hydrocarbons are identified to be present, and the DAEM is used as a soft-conditioning trend for the geostatistical population. In examples having three-dimensional seismic data of suitable resolution over the DAEM depth of investigation volume, the seismic evaluation is based upon the relationship of the seismic and reservoir properties established in offset wells and can be used as an additional conditioning parameter to the three-dimensional geostatistical population. In some embodiments, based on the results of the above described evaluation, new wells are targeted at pools of undrilled oil, existing wells are recompleted to try and access the oil or the implications of oil at that location may be used to propose other analogous targets elsewhere in the field.

[0052] FIGS. 14 and 15A-15C depict various methods in accordance with the present techniques. While the various blocks in FIGS. 14 and 15A-15C are presented and described sequentially, some or all of the blocks may be executed in different orders, may be combined or omitted, and some or all of the blocks may be executed in parallel. Furthermore, the blocks may be performed actively or passively.

[0053] FIG.14 illustrates a flow chart that shows an example method 1400 for determining a saturation model of the formation of FIG.1. In some embodiments, the DAEM tool described in the disclosure is implemented to build a saturation solution for a formation to predict saturation for the halo of investigation and beyond based on an integration of well log IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) measurements, DAEM data sets, and seismic information. At block 1405, the method 1400 includes building a stratigraphic framework of structural surfaces and their reservoir and non- reservoir zones in the formation. The method 1400 alternatively establishes a working dataset by collecting well data, seismic data, and DAEM data using different DAEM tools with varying DODs. The method 1400 may use the well data and seismic data to define a plurality of major (e.g., seismic) and minor (e.g., well-log-based) boundaries for building a working stratigraphic model applicable to the halo around the wellbore. The method 1400 optionally populates a three-dimensional grid with discrete reservoir rock types or continuous bulk properties using one or more geostatistical techniques per reservoir zone.

[0054] At block 1410, the method 1400 includes building a well based rock model. For example, examples of the method 1400 includes confirming and populating the rock-type classes from offset wells (or extracted from log-seismic relationships) in the three-dimensional framework. As another example, the method 1400 analyzes offset wells to define a plurality of anisotropy classes for the formation in the working stratigraphic model applicable to the halo around the wellbore.

[0055] At block 1415, the method 1400 includes implementing a DAEM integration, such as integrating a plurality of models including DAEM surface depth and resistivity information into the three-dimensional framework to generate a reservoir saturation solution within the halo of DAEM investigation around the wellbore. Examples of the method 1400 include generating the saturation solution in the DAEM halo or beyond the DAEM halo based on well data only or based on both well data and seismic data.

[0056] At block 1420, the method 1400 includes applying a geophysical validation, such as generating a well-based synthetic impedance response and a synthetic seismic response to IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) validate the saturation solution in the DAEM halo or beyond the DAEM halo by comparing to an observed impedance response and an observed seismic response, respectively.

[0057] Particular embodiments repeat one or more steps of the method of FIG. 14, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG.14 as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG.14 occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to determine a saturation model of the formation of FIG. 1 using the DAEM tool described in the disclosure, including the particular steps of the method of FIG. 14, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG.14, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG.14, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG.14.

[0058] FIG.15A illustrates a flow chart that shows a method 1500 for building a stratigraphic framework for determining the saturation model of the formation of FIG. 1. In some embodiments, the method 1500 is implemented to build a stratigraphic framework to determine a saturation model for the formation within the halo of DAEM investigation around the wellbore. At block 1502, the method 1500 includes establishing a working dataset for the stratigraphic framework, such as by using well data to establish well type and stratigraphic trends from different depositional environments in the stratigraphic framework. In another example, the method 1500 uses both well data and seismic data to build the stratigraphic framework. At block 1504, the method 1500 includes determining a pre-drill model of surfaces and stratigraphic units; an example of which uses well data only to predict isochores and build IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) the stratigraphic model. As another example, the method 1500 may use both well data and seismic data to reconcile seismic resolution and interpretation requirements for the stratigraphic framework. In the alternative, this information is determined using well data alone.

[0059] At block 1506, the method includes creating a working three-dimensional grid for a halo of DAEM investigation around the wellbore and beyond. In particular, the method 1500 may build a suitably scaled grid based on well objectives, input data, geological variation, and investigation volume in the stratigraphic framework.

[0060] Particular embodiments optionally repeat one or more steps of the method of FIG.15A, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG.15A as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 15A occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to build a stratigraphic framework for determining the saturation model of the formation of FIG.1 using the DAEM tool described in the disclosure, including the particular steps of the method of FIG. 15A, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG.15A, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 15A, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG.15A.

[0061] FIG. 15B is a flow chart of an example method 1510 for building a well based rock model for determining the saturation model of the formation of FIG.1. In some embodiments, the method 1510 is implemented to build a well based rock model to determine a saturation model for the formation within the halo of DAEM investigation around the wellbore. At block IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) 1512, the method 1510 includes applying recognition of rock fabric and three-dimensional spatial population. In an example, the method 1510 uses the well data only from offset wells or a build section of the wellbore to define a plurality of rock-fabric saturation classes. In another example, the method 1510 uses the seismic data to classify a plurality of seismic acoustic impedance (AI) facies and extract spatial variography / trends from the seismic data. The method 1510 optionally populates the three-dimensional grid with the plurality of rock-fabric saturation classes from offset wells or extracted from log-seismic relationships. At block 1514, the method defines a plurality of resistivity anisotropy classes by analyzing the well data from the offset wells or a build section of the wellbore.

[0062] Particular embodiments may repeat one or more steps of the method of FIG.15B, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG. 15B as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 15B occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to build a well based rock model for determining the saturation model of the formation of FIG. 1 using the DAEM tool described in the disclosure, including the particular steps of the method of FIG. 15B, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG.15B, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 15B, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG.15B.

[0063] FIG. 15C is a flow chart that of an example method 1520 for implementing a DAEM integration for determining the saturation model of the formation of FIG. 1. In some IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) embodiments, the method 1520 is implemented to integrate DAEM surface depth and resistivity information into the three-dimensional framework. At block 1522, the method 1520 includes applying a rationalization of DAEM conductivity output, which in this example, includes extracts depth, dip, and faulting information for a plurality of bed boundaries in the DAEM halo of investigation and beyond to match with existing stratigraphy based on well data only or a process based upon well data and three-dimensional seismic volumes.

[0064] At block 1524, the method 1520 includes updating a depth model, that in an example, when the well data only is used, confirming a plan to update the depth model used for depth treatment at and beyond a maximum DOD. As another example, both the well data and the seismic data are used, the method 1520 inverts the DAEM data to identify resistivity defined bed boundaries and uses the bed boundaries detected by DAEM to update a total depth (“T- D") model and revise a seismic depth model.

[0065] At block 1526, the method 1520 includes updating a surface model during or after drilling by determining a plurality of stratigraphic boundaries defined from DAEM data which are represented by elongated depth ribbons. In alternatives, the method 1520 updates the surface model by merging the plurality of DAEM depth ribbons and a plurality of three- dimensional model surfaces.

[0066] At block 1528, the method 1520 includes updating a three dimensional grid. For example, the method 1520 rebuilds the three dimensional grid when the well data only is used. As another example, the method 1520 may resample the impedance and the three dimensional seismic volume to update the three dimensional grid when both well data and seismic data are used.

[0067] At block 1530, the method 1520 includes updating a rock model. For example, the method 1520 uses vertical resistivity (Rv) and horizontal resistivity (Rh) to refine the rock IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) model when the well data only is used. As another example, the method 1520 uses Rv, Rh, and seismic data to refine the rock model when both well data and seismic data are used.

[0068] At block 1532, the method 1520 includes defining potential / inferred hydrocarbon datums. For example, the method 1520 defines a plurality of pre-drill datums with DAEM, drill information, or a combination thereof to generate height above the free-water level of any hydrocarbon accumulation.

[0069] At block 1534, the method 1520 includes applying the population of the saturation model in the DAEM halo. The method 1520 optionally determines the saturation of each cell in the three-dimensional grid using both the rock fabric (the porosity and / or rock-type index) and resistivity properties. For example, the method 1520 determines an initial saturation estimation from the resistivity, porosity, and rock-type values sampled at each cell of each zone within the high confidence DAEM halo. As another example, the method 1520 identifies and extrapolates possible hydrocarbon water contacts in individual reservoir zones in this initial solution. In an embodiment, the method 1520 propagates the updated saturation solution in the DAEM halo.

[0070] At block 1536, the method 1520 includes applying saturation validation, which optionally includes extracting pseudo well profiles in the three dimensional grid and validating the extracted pseudo well profiles.

[0071] At block 1538, the method 1520 includes populating the saturation model beyond the DAEM halo. In particular, the method 1520 populates the updated saturation solution away from the study well halo into the larger three dimensional volume.

[0072] Particular embodiments may repeat one or more steps of the method of FIG. 15C, where appropriate. Although this disclosure describes and illustrates particular steps of the IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) method of FIG.15C as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 15C occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to implement a DAEM integration for determining the saturation model of the formation of FIG.1 using the DAEM tool described in the disclosure, including the particular steps of the method of FIG. 15C, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG.15C, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 15C, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG.15C.

[0073] FIG. 15D is a flow chart that shows a method 1540 for applying a geophysical validation for determining the saturation model of the formation of FIG. 1. In some embodiments, the method 1540 is implemented to apply a geophysical validation for the saturation solution. At block 1542, the method 1540 includes validating a synthetic impedance response based on a resistivity model. For example, the method 1540 calculates a well based impedance synthetic from the resistivity model when the well data only is used. As another example, the method 1540 determines a new impedance model when both the well data and the seismic data are used.

[0074] At block 1544, the method 1540 includes validating a synthetic seismic response. For example, the method 1540 generates a synthetic seismic response when the well data only is used. As another example, the method 1540 generates a synthetic seismic response for comparison to the three-dimensional volume and estimates errors in the depth domain. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236)

[0075] Particular embodiments repeat one or more steps of the method of FIG. 15D, where appropriate. Although this disclosure describes and illustrates particular steps of the method of FIG. 15D as occurring in a particular order, this disclosure contemplates any suitable steps of the method of FIG. 15D occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method to apply a geophysical validation for determining the saturation model of the formation of FIG. 1 using the DAEM tool described in the disclosure, including the particular steps of the method of FIG. 15D, this disclosure contemplates any suitable method including any suitable steps, which may include all, some, or none of the steps of the method of FIG.15D, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 15D, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG.15D.

[0076] The present invention described herein, therefore, is well adapted to carry out the objects and attain the ends and advantages mentioned, as well as others inherent therein. While a presently preferred embodiment of the invention has been given for purposes of disclosure, numerous changes exist in the details of procedures for accomplishing the desired results. These and other similar modifications will readily suggest themselves to those skilled in the art, and are intended to be encompassed within the spirit of the present invention disclosed herein and the scope of the appended claims. IM-#10707527.1

Claims

Attorney Docket No.: 65REL-510197-WO-2 (000236) CLAIMS What is claimed is:

1. A method of managing a hydrocarbon bearing subterranean formation comprising: obtaining data about reservoirs in the subterranean formation comprising, deep azimuthal electromagnetic (“DAEM”) data, seismic data, and offset well data; generating a stratigraphic framework of the reservoirs based on an inversion of the DAEM data; populating rock types into the stratigraphic framework based on the offset well data to form an updated stratigraphic framework; and integrating the DAEM data into the updated stratigraphic framework to generate a saturation model solution.

2. The method of Claim 1, further comprising locating and extracting hydrocarbons in the formation based on the saturation model solution.

3. The method of Claim 1, wherein the offset well data comprises at least one of seismic acoustic impedance data and resistivity data.

4. The method of Claim 1, further comprising determining water saturation in the formation by combining the DAEM data and the seismic data.

5. The method of Claim 1, wherein the DAEM data is obtained by imaging the subterranean formation from within a test wellbore that intersects the subterranean formation.

6. The method of Claim 5, wherein an offset wellbore is formed in the formation distal from the test wellbore.

7. The method of Claim 5, wherein the DAEM data is obtained using a resistivity tool.

8. The method of Claim 5, wherein the stratigraphic framework is generated at a time selected from the group consisting of before forming the test wellbore and after forming the test wellbore, and wherein when obtained before when the test wellbore is formed, the stratigraphic framework is updated with information obtained by imaging the test wellbore.

9. The method of Claim 1, further comprising drilling a wellbore into a designated location in the subterranean formation based on the saturation model solution.

10. The method of Claim 1, further comprising identifying and extrapolating possible hydrocarbon water contacts in individual reservoir zones. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) 11. A system of managing a hydrocarbon bearing subterranean formation comprising: a processor; and a computer-readable non-transitory storage medium comprising instructions that, when executed by the processor, cause the processor to perform operations comprising: obtaining data about reservoirs in the subterranean formation comprising, deep azimuthal electromagnetic (“DAEM”) data, seismic data, and offset well data; generating a stratigraphic framework of the reservoirs based on an inversion of the DAEM data; populating rock types into the stratigraphic framework based on the offset well data to form an updated stratigraphic framework; and integrating the DAEM data into the updated stratigraphic framework to generate a saturation model solution.

12. The system of Claim 11, the operations further comprising locating and extracting hydrocarbons in the formation based on the saturation model solution.

13. The system of Claim 11, wherein the offset well data comprises at least one of seismic acoustic impedance data and resistivity data.

14. The system of Claim 11, the operations further comprising determining water saturation in the formation by combining the DAEM data and the seismic data.

15. The system of Claim 11, wherein the DAEM data is obtained by imaging the subterranean formation from within a test wellbore that intersects the subterranean formation.

16. The system of Claim 15, wherein an offset wellbore is formed in the formation distal from the test wellbore.

17. The system of Claim 15, wherein the DAEM data is obtained using a resistivity tool.

18. The system of Claim 15, wherein the stratigraphic framework is generated at a time selected from the group consisting of before forming the test wellbore and after forming the test wellbore, and wherein when obtained before when the test wellbore is formed, the stratigraphic framework is updated with information obtained by imaging the test wellbore.

19. The system of Claim 11, the operations further comprising: drilling a wellbore into a designated location in the subterranean formation based on the saturation model solution; and identifying and extrapolating possible hydrocarbon water contacts in individual reservoir zones. IM-#10707527.1Attorney Docket No.: 65REL-510197-WO-2 (000236) 20. A non-transitory computer-readable medium comprising instructions that are configured, when executed by a processor, to perform operations comprising: obtaining data about reservoirs in the subterranean formation comprising, deep azimuthal electromagnetic (“DAEM”) data, seismic data, offset well data; generating a stratigraphic framework of the reservoirs based on an inversion of the DAEM data; populating rock types into the stratigraphic framework based on the offset well data to form an updated stratigraphic framework; and integrating the DAEM data into the updated stratigraphic framework to generate a saturation model solution. IM-#10707527.1

Images