Integrated geomechanical model for predicting hydrocarbons and migration pathways

Abstract

The invention relates to a method for predicting hydrocarbon accumulation in a geological region, the method comprising the steps of: a. generating a geological basin model; b. generating a geomechanical model; c. generating a combined model; d. generating a strain map based on the information obtained in steps a to c; e. predicting hydrocarbon accumulation from the strain map.

Description

Technical Field

[0001] This invention relates to a method for predicting oil and gas accumulation in geological regions. This prediction method can improve oil and gas production by predicting the location and migration trajectory of oil and gas accumulations, thus providing a useful tool for exploration and field development (FDP) programs. Background Technology

[0002] This invention relates to the field of predicting the location of oil and gas accumulations. The occurrence and movement of such accumulations depend on the geological formation of multiple geological layers in a given geographical area, as well as the corresponding physical and geological properties of that area. Due to the high cost of drilling for oil and gas extraction, various methods have been developed in this field to simulate and predict the occurrence of oil and gas accumulations. Different simulation techniques are employed in these methods.

[0003] Reference WO 2010 / 120492 A2 relates to a computer-implemented method for conducting geological basin analysis to determine the accumulation of oil and gas in a subsurface region of interest. The method includes: defining a basin analysis project associated with at least one basin within the subsurface region of interest, using project scope data and geological and geophysical data related to the subsurface region of interest, in an integrated computer environment having at least one graphical user interface and multiple basin analysis workflows; each basin analysis workflow has user-selectable tasks. The method further includes applying at least one basin analysis workflow to the basin analysis project in the integrated computer environment and executing user-selected tasks to perform basin analysis, which includes determining basin characteristics, geological trends, and the likelihood of hydrocarbon systems; wherein the use of basin analysis workflows is based on the amount of data provided by the user through the execution of selected tasks and the basin analysis project scope data.

[0004] Reference US 7,054,753 B1 relates to a method for locating oil and gas drilling prospects using an unprecedented amount of digitized logging data, well production history, well test data, and any other relevant digitized well data. The method includes: obtaining logging data from multiple wells drilled in a desired oil and gas basin, and then digitizing it on a computer or other suitable digitizing device; then normalizing the logging data for each well using a standardized scale; correlating each digitized logging log to establish a stratigraphic framework for the entire basin; and identifying observable sedimentary features and sedimentary facies for each interval of each well. The method also includes visually displaying multiple individual logging logs to reveal consistent sedimentary features across a cross-sectional area of ​​a portion of the basin.

[0005] However, an improved method is needed to predict the occurrence and movement of oil and gas accumulation.

[0006] Therefore, the object of the present invention is to provide an improved method for predicting oil and gas accumulation in geological regions. Summary of the Invention

[0007] The above problems are at least partially solved by a method for predicting hydrocarbon accumulation in geological regions, which includes the following steps:

[0008] a. Generate geological basin models;

[0009] b. Generate a geomechanical model;

[0010] c. Generate a comprehensive model;

[0011] d. Generate strain maps based on the information obtained in steps a to c;

[0012] e. Predicting oil and gas accumulation from strain diagrams.

[0013] Spatial and temporal prediction of oil and gas accumulation is possible. Geographic field maps are overlaid with strain maps and / or oil and gas accumulation maps. Therefore, a spatial correspondence between spatial strain maps and / or oil and gas accumulation maps and geological regions can be established. Consequently, different borehole locations can be obtained, and costly drilling at multiple locations can be avoided.

[0014] In a preferred embodiment, the geological basin model further includes at least one of the following steps:

[0015] a. Determine the stratigraphic level and faults;

[0016] b. Recovery and re-mining to identify construction events;

[0017] c. Porosity modeling;

[0018] d. Stress modeling;

[0019] e. Modeling the porosity-permeability relationship.

[0020] This invention provides an improved basin model to include all geological features and is based on structural reconstruction for timely application of tectonic events. By using petroleum system modeling techniques, combined with seismic, well data, and geological knowledge, sedimentary basin evolution is simulated, and predictions of pore pressure and porosity in the resource assessment area are performed. The goal of this phase is to create a basin history that includes geological structures, serving as the basis for the next phase of geomechanical modeling (see [link]). Figure 1 Strata (also known as surfaces) and faults were interpreted from seismic data and derived from isopyre maps. These maps were used to construct basin models from the top surface sediments to the reservoir. The evolution of porosity, pore pressure, temperature, and thermal maturity over time was simulated and calibrated against measurement data.

[0021] In this invention, forward modeling and reconstruction tools can be used to validate existing 3D interpretations and structural models. The results reveal the geometry and timing of fault movement, and this is relevant to all subsequent basin modeling steps. In this invention, for example, a regional-scale 3D reconstruction of the larger Abu Dhabi area is performed, and geometric and geomechanical algorithms are used to capture time-varying geological strain to analyze strain at different time steps during, for example, the tectonic-stratigraphic evolution of the Abu Dhabi Basin. Simulation results provide estimated porosity and pore pressure, as well as a reconstruction of the entire basin geometry over time. The resulting model is then used as the basis for a further fracture prediction stage; the results ultimately agree with faults derived from existing seismic interpretations. Model porosity, pore pressure, and predicted fractures are used in the development of static geological and dynamic reservoir models. The application of petroleum system modeling techniques is crucial for reconstructing basin paleogeometry and its impact on the geological evolution of porosity and pressure. Geological knowledge, such as the present-day basin geometry and formation age, must be obtained before reconstructing the basin geometry. In the model simulation step, the model is reverted to the oldest formation (see...). Figure 2 ).

[0022] Chilingarian & Wolf (1975) studied the porosity-permeability relationship, finding that the permeability of isotopic sediments is controlled by their porosity and grain size distribution. Further research by Tissot and Welte (1984) showed that porosity at shallower depths rapidly diminishes with further compaction. However, the rate of porosity loss decreases with increasing pressure. To predict pressure, a porosity-permeability relationship, a piecewise linear function in a permeability vs. porosity plot, is used to control the pressure model.

[0023] In a preferred embodiment, the pressure modeling step further includes at least one of the following steps:

[0024] a. Calibration of the pore pressure model;

[0025] b. Application of pore pressure models in geological regions.

[0026] Model porosity depends on burial depth, the weight of the overburden sediment column, and lithological characteristics. Porosity calibration was achieved by adjusting the compaction curve to effective stress. Pore pressure was calibrated by adjusting the lithological porosity-permeability relationship. Low-permeability lithology leads to high pore pressure. Proper definition of lithology and / or sedimentary facies is necessary for each formation. Lithological parameters, such as mechanical compaction and permeability, are unique to each formation. These parameters control the deformation and compaction behavior of each formation across all geological ages during the simulation. When defining boundary conditions, paleowater depth, sediment-water interface temperature, and heat flow are important factors constraining the geometry and thermal evolution of the basin at each specific geological age.

[0027] In a preferred embodiment, the geological basin model includes mechanical stratigraphy. In a preferred embodiment, the geological basin model includes a permeability modeling step.

[0028] In a preferred embodiment, the geological basin model further includes at least one of the following steps:

[0029] a. Decomposition of sediments;

[0030] b. Obtain the burial history of the geological region.

[0031] Sediment decomposition was modeled, allowing for the reconstruction of stratigraphic structure over time. Athy (1930) first described a simple porosity-depth relationship. He stated that porosity Φ decreases exponentially with depth as the compaction coefficient k increases. Smith (1971) improved this definition and proposed using effective stress instead of total depth in compaction calculations. Athy's law, expressed in terms of effective stress, was used to calculate pore pressure in a forward modeling simulator. Information such as stratigraphic age, erosion events, and periods of discontinuity were considered during the simulation.

[0032] In a preferred embodiment, the geological basin model includes a step of geological region overpressure modeling. Formation overpressure is observed at greater depths, and its modeling relies on the evolution of native water vectors over geological time. These vectors depend on multiple lithological parameters and capillary intrusion pressures of adjacent model layers.

[0033] In a preferred embodiment, generating a geomechanical model includes at least one of the following steps:

[0034] a. Seismic inversion and detailed rock physics analysis, including fluid substitution modeling;

[0035] b. Pre-stack seismic data conditioning;

[0036] c. Pre-stack AVO simultaneous inversion;

[0037] d. Predicting mechanical properties based on porosity correlation derived from core results;

[0038] e. Generate a one-dimensional geomechanical model.

[0039] This primarily involves a one-dimensional geomechanical step (wherever available) based on curves calibrated using Rock Mechanics Testing (RMT). A three-dimensional geomechanical model is then created based on porosity provided by the rock physics model and elastic parameters obtained from seismic inversion. The first stage involves seismic inversion, the one-dimensional geomechanical model, and the three-dimensional model. Seismic data provides the best-in-class high-resolution spatial measurements, which are then used to construct the structural framework and calculate an accurate three-dimensional property model. Pre-stack seismic inversion enables the calculation of rock mechanical properties, such as Poisson's ratio, from seismic data (which serves as input to the three-dimensional geomechanical model). This step includes detailed rock physics analysis covering fluid substitution modeling, pre-stack seismic data conditioning, and simultaneous pre-stack AVO inversion. Technical details of the above options are given below.

[0040] Pre-stack AVO simultaneous inversion

[0041] The following list shows the data required for AVO inversion:

[0042] Well data:

[0043] • Standard E-logs (sonic waves, shear sonic waves, and density) for selected wells in LAS format.

[0044] • Selected well formation top and markers in ASCII format.

[0045] • Perform rock physical assessment of selected wells in LAS format.

[0046] • Check the jetting data of the selected well in LAS format.

[0047] • Process VSP corridor stacks in SEGY format, plus processing reports for selected wells.

[0048] • ASCII format for well location and deviation survey of selected wells (more suitable for vertical wells far from major faults).

[0049] • Reservoir fluid parameters: pressure, temperature, formation water salinity, gas-water ratio, gas specific gravity, etc.

[0050] Any other information related to well data processing.

[0051] Earthquake data:

[0052] • Angle stacking (minimum near, middle, far) correction to an appropriate reference in SEGY format

[0053] • Seismic velocities in SEGY format (based on the same reference as seismic data),

[0054] • Collect and process reports.

[0055] Rock Physics

[0056] The velocity of sound in a reservoir varies with rock lithology / mineralogy, porosity, pore type, clay content, fluid saturation, stress, temperature, and the frequency at which measurements are performed. Rock physics analysis is used to assess and understand the effects of lithology, porosity, and fluid on the velocity of sound and density.

[0057] Consistency between logging conditions and field data

[0058] Where possible, detailed logging editing and depth-time conversion were performed on selected wells, starting with the original field logging. The reliability of sonic and density logging was primarily verified by referencing adjacent logging sections less affected by adverse well conditions and fluid intrusion processes (i.e., gamma-ray logging, resistivity logging, and neutron porosity logging). Editing of unreliable logging zones was performed based on multivariate statistical parametric regression methods correlated with other relevant logging data. Unreliable depth intervals were analyzed and edited using a series of statistical, empirical, and multi-log / multi-well data substitution techniques, as shown below. Checkshot and VSP data were evaluated and edited as needed to generate the depth-time conversion function before calibrating the sonic logging. Well acoustic impedance (in the time domain) was tested to ensure it provided accurate measurements of rock acoustic properties across the entire length of the logging borehole and was correctly calibrated to needle borehole seismic logging. This involved objective comparisons with surface and borehole seismic logging. Where discrepancies arose, the method was iterated through data validation and editing cycles until the logging curves and time-depth function were considered to have reached optimal reliability. Plot the final edited logging curves against depth for all wells in the study area to ensure consistency of field data. Investigate out-of-field data trends for anomalous wells. Anomalous wells may have a valid geological cause. If not, corrections will be needed early in the study to rectify erroneous data and ensure consistency across the entire area.

[0059] Rock elasticity analysis

[0060] Detailed rock elasticity analysis was performed using data from selected wells to determine whether there was a significant correlation between elastic properties (acoustic impedance, Poisson's ratio, and density) and rock physical data (such as porosity).

[0061] Angular stacking alignment

[0062] A proprietary algorithm called Non-rigid Matching (NRM) can be used to align angular stacks or flatten NMOs (this expression of the NMO velocity of P-waves is valid for anisotropy of any intensity, Tsvankin (1997)), correct angular collections, and thus eliminate any residual NMO and possible anisotropic effects. In anisotropic media, the velocity of seismic waves varies with the propagation angle, while the NMO velocity is calculated with respect to a zero offset point. The idea is to apply an anisotropic ray tracing algorithm to calculate the ray velocity of each ray and estimate the NMO correction for each ray. NRM stretches and squeezes sample by sample, essentially aligning any number of traces to a reference trace. Typically, stacked traces close to the offset are calculated, and each trace in the collection is matched directly or recursively to them. Thus, NRM attempts to flatten all events; it is neither horizon-driven nor movement-driven. Better alignment of events in angle collection should lead to more reliable AVO (amplitude varies with angle, meaning the amplitude varies with the offset caused by fluid lithology. AVO is also known as AVA (amplitude varies with angle) because this phenomenon is based on the relationship between the reflection coefficient and the angle of incidence) properties, especially for high-angle applications (triple AVO).

[0063] Wavelet estimation

[0064] Wavelet estimation was performed to estimate wavelets from seismic data stacked from each input angle using well elastic data. Wavelets were estimated from seismic trajectories and well reflectances. Well reflectances were calculated using approximations from Aki and Richards. Wavelet estimations were tested under various time windows and multi-well scenarios. Quality control of the wavelet estimation results was performed using well-seismic combination display and matched statistics. Furthermore, different wavelets were tested through inversion to select the optimal wavelet.

[0065] Low-frequency modeling

[0066] Due to the geometry of the acquired data, seismic reflection data is band-limited at both ends of the spectrum. The missing lower side of the spectrum is crucial. Therefore, all seismic inversion schemes in the industry (post-stack or pre-stack) require a low-frequency model (LFM) to calculate the elastic properties across the entire frequency band for direct comparison and calibration with well logging curves. Furthermore, the accuracy of the elastic properties (AI (acoustic impedance), Vp / Vs (Vp and Vs: compression and shear velocities), and density) inverted from seismic inversion depends on the accuracy of the LFM. Therefore, ensuring the LFM is as accurate as possible, especially within the inter-well space, is critical. By inferring appropriate well logging data, using interpreted stratigraphic positions as guidance, and then performing low-pass filtering, a low-frequency model is derived for each property (AI, Vp / Vs, and density). The low-frequency model may also be constrained by seismic velocities (e.g., stacking or migration velocities), seismic properties (such as relative AI volume, depth trends, and dip angles estimated from seismic data and / or observed stratigraphic relationships).

[0067] Global simultaneous AVO inversion

[0068] Simultaneous inversion is performed using global simultaneous AVO inversion. By using separate wavelets for each partial stack, the frequency and phase differences between partial stacks are directly processed, ensuring maximum resolution results for each layer characteristic; for example, Poisson's ratio has higher resolution than distant partial stacks. No frequency balancing or special phase adjustment of the seismic data is required before inversion. During simultaneous AVO inversion, high-frequency variations in the reflection angle (e.g., in high-velocity or low-velocity layers) are estimated based on the estimated acoustic impedance, Vp / Vs, and density (density depends on the available angular range in the input seismic data) to more accurately estimate layer characteristics (see [link to relevant documentation]). Figure 11 Prior to full inversion production, extensive inversion tests and validations were performed on selected logging data to determine the optimal ones.

[0069] · Xiaobo,

[0070] • Inversion parameters.

[0071] In reservoir zones, the prediction of mechanical properties is based on porosity correlations derived from core results (see [link to core analysis]). Figure 12 ).

[0072] • Porosity cube derived from reservoir model;

[0073] • In tight units of caprock and segregated reservoir zones, prediction of mechanical properties is based on co-kriging amplified logging;

[0074] • Mechanical property profile derived from a one-dimensional geomechanical model.

[0075] Generate a one-dimensional geomechanical model

[0076] For example, many of the one-dimensional models that have been developed are based on the Abu Dhabi oil field (see...). Figure 13 The mechanical properties and stresses derived from well logging in the one-dimensional model are used to construct the three-dimensional geomechanical model. The entire model is calibrated whenever RMT data is available. In seismically driven geomechanical property modeling, laboratory test results are correlated / connected with seismic inversion outputs. The construction process of the one-dimensional geomechanical model for new wells includes:

[0077] • Organize, review, and validate input data from off-wells;

[0078] • Loading and QC-available logging data;

[0079] • Identify and characterize stress-induced wellbore events based on time, depth, and the weight of mud used;

[0080] • Using available well logging and core test data, construct rock elasticity and strength property models for overburden and reservoir sections.

[0081] • Use the most appropriate correlation to establish the elastic and rock strength property profiles derived from well logging (see...) Figure 14 These correlations are driven by rock mechanics test results and are combined with new additional laboratory core tests (if performed).

[0082] • Estimate the wellbore pore pressure profile. Utilize density, sonic and resistivity logging, local correlation, MDT (Modular Formation Dynamics Tester), and DST (Drill Stimulation Test) data (if available), and determine the profile using existing pore pressure data as constraints.

[0083] • Use available images of hydraulic fractures induced during drilling or microfracture and / or directional wellbore data (if available) to determine the orientation of the horizontal stress direction;

[0084] • Develop continuous profiles of major geostresses, showing the magnitude of overburden stresses and the maximum and minimum horizontal stresses. The magnitude of horizontal stresses is determined using a porosity-elastic horizontal strain model. The magnitude of horizontal stresses is calibrated using high-quality LOT (Leak Test) / ELOT (Extended Leak Test) data and rigorously validated based on back analysis of wellbore fractures and drill-induced fractures observed on image logging and fracture analysis on caliper logging (if available). Data utilized include density and sonic logging, LOT / ELOT data, image logging, caliper logging, drilling daily reports, mud daily reports, bottom hole reports, structural geology, and local correlations and knowledge.

[0085] • The geomechanical model was validated through rigorous historical matching with image logging, drilling experience, field observations and measurements, and well test data;

[0086] • Based on the available models for different reservoirs, RMT samples are selected to fill any gaps used for integrated model calibration;

[0087] The next steps include characterizing rock heterogeneity at the core and logging scales and completing a quality assessment (based on mechanical anisotropic elastic properties, minimum horizontal stress estimation, and rock-fluid interactions).

[0088] • Characterize the vertical heterogeneity of the overall reservoir strata;

[0089] • Improve resource definition by measuring and modeling key reservoir characteristics (porosity, permeability, pore space composition, etc.);

[0090] • Improve resource recovery by quantifying the mechanical behavior of reservoir and surrounding rock structures and by determining the rock-fluid interaction parameters required to understand fluid behavior;

[0091] • Implement new testing activities to better characterize the mechanical behavior of highly porous carbonates;

[0092] • Collecting high-quality horizontal stress values ​​requires performing microfracture testing in a vertical well, while simultaneously conducting pore pressure measurements (before microfracture testing) and borehole image (BHI) logging (before and after microfracture testing);

[0093] • Evaluate the properties of fault materials (see Figure 1 (See the red box below);

[0094] • Characterize fractures by reanalyzing all BHI logs and performing shear tests on cores;

[0095] • Perform a specialized casing hole integrity analysis;

[0096] • Perform blind testing to validate the previous model;

[0097] • Enhanced processing and interpretation of borehole image logging (see...) Figure 15 ).

[0098] • Loading, processing, and QC of image logging data. The image logging data to be evaluated is depth-matched with the final open-hole logging set and oriented within the borehole reference frame;

[0099] • Perform a quality assessment on the provided image data. This includes evaluating the provided orientation data to ensure correct feature orientation, examining the extent of non-geological imagery and logging artifacts, and their impact on the level of geological analysis utility.

[0100] • All geological features were manually selected using sinusoidal curve fitting techniques; lithology and type were selected based on sedimentary bedding features, and structural descriptors were used for faults, fractures, classification picks, and deformation-related features (including soft sediment deformation). Drilling-induced features were also selected and oriented. Confidence ratings were assigned to the selected features.

[0101] • Structural Interpretation – Following the generation of a manually dip-selected dataset, a detailed structural analysis was performed to define the overall tectonic geometry described by lithological bedding and to identify any folds, faults, or other deformations that might not be directly imaged. This analysis characterizes the overall trends and relationships of faults, allowing for the identification of fault-related fracture density variations and stress variations. The specific structural analysis includes the following steps:

[0102] • Subdivide the examined depth range based on geological structures (e.g., identifying fracture patterns and orientations, structural dip zones, fault zones, unconformities, etc.);

[0103] This is accomplished through visual tilt assessment, vector azimuth diagrams, and stereoscopic views;

[0104] • Identification and location of structural features. Identification and tabulation of unconformities, fault zones, fractures, and deformed strata;

[0105] • Fault zones are characterized based on depth, orientation, strike, rotation axis, lithology, presence or absence of drag zones, and width of possible associated damage. Where possible, infer a sense of slippage.

[0106] • The structure of each well was analyzed to ensure a clear understanding of the wellbore orientation and image features.

[0107] In a preferred embodiment, the prediction of mechanical properties based on the porosity correlation derived from the core results further includes at least one of the following:

[0108] a. The porosity cube is derived from the reservoir model;

[0109] b. In tight units within caprock and segregated reservoir zones, prediction of mechanical properties is based on co-kriging amplified logging; and

[0110] c. The mechanical property profile is derived from a one-dimensional geomechanical model.

[0111] In a preferred embodiment, the method for predicting hydrocarbon accumulation in a geological region further includes the step of creating a structural model, wherein the method further includes the step of estimating the three-dimensional static and dynamic aspects of the geomechanical model. In a preferred embodiment, the method for predicting hydrocarbon accumulation in a geological region further includes the step of fault and fracture analysis.

[0112] Some strata show strong indications of the possible presence of natural fracture networks within these reservoirs. An attempt was made to develop multi-scale fracture models for each stratum, with the aim of integrating them into a three-dimensional geomechanical model.

[0113] • The fracture model was established by integrating all well rock physical data, image logging data, geomechanical data, core data, seismic data, and well test data from all wells drilled at the start of the project.

[0114] The following assumptions and workflows were applied:

[0115] • Matrix models exist for three-dimensional geological models. Since DFN (Discrete Fracture Network) cannot be extended to very fine geological models, if the geological model is very fine, an extended model for flow simulation is needed to build and extend the DFN-based fracture model.

[0116] • Only wells from which fractures are interpreted from image logging are used to establish DFN.

[0117] • If fracture pore size interpretation has been completed in a well with BHI logging using advanced fracture interpretation, it can be used as input for DFN. If fracture pore size has been measured from conventional core samples, it can also be used as input.

[0118] • Seismic data with horizons and faults are available in the depth domain, which can be used as input for fracture interpretation. Seismic interpretation based on velocity models is not included.

[0119] • The geomechanical model uses a 3D model, a 3D geological model, and a scaled-up simulation model based on full-well data as input.

[0120] • Use 3D seismic data in the depth domain, where horizon and fault interpretation are performed in the depth domain.

[0121] • Fracture interpretation was compiled from image logging, open fractures were isolated and loaded into a 3D geological static model. Fracture orientation was studied from rosette diagrams of each stratigraphic group. Furthermore, stereographic charts of all open fractures were prepared for each stratigraphic group.

[0122] • Analyze the above charts to attempt to link the crack groups with the tectonic history of the area / region (this requires a reconstruction model). Figure 1 (See middle box 5) to understand how many tectonic events there are. Determine how many sets of fractures to model for each formation, separate the fracture data into groups, and associate each group with its tectonic events.

[0123] • Generate a fracture intensity log for each fracture group. If available in directional cores, perform a similar analysis on fractures in conventional core descriptions.

[0124] • Use fracture intensity to plot Poisson's ratio and Young's modulus logging curves to observe the existence of geomechanically controlled interlayer fractures.

[0125] • This study interprets faults and their relationship with BHI-interpreted fracture corridors. Coherence / similarity seismic properties are generated in the depth domain, and the existence of fracture corridors is investigated. If such corridors exist, Petrel Ant-Tracking is performed to interpret them. Simultaneously, curvature properties are generated to characterize the fracture corridors.

[0126] In a preferred embodiment, the method for predicting oil and gas accumulation in a geological region further includes the following steps:

[0127] a. Generate discrete crack networks;

[0128] b. Extend the discrete fracture network into a static geomechanical model.

[0129] Based on the data analysis results, an attempt was made to generate a multi-scale crack model, which includes:

[0130] o Large-scale fractures that cut across strata, represented by faults.

[0131] o Extract fracture corridors associated with faults from seismic properties

[0132] DFN was performed on interlayer geomechanical control fractures using sedimentary facies models, stiffness modulus models (using Young's modulus and Poisson's ratio logging from the well), and fracture strength logging from the well.

[0133] o Small-scale diffusion fractures best seen from the core.

[0134] • Assign fracture pore size and permeability to DFN based on data availability.

[0135] • Create three different implementations of DFN to cover possible uncertainties.

[0136] • The developed DFN is scaled up into an amplified static model to generate fracture porosity and fracture permeability tensors (see [link]). Figure 16 ).

[0137] • The uncertainty of the three-crack model is amplified and applied to the above static model.

[0138] • Vertical variation of fracture density in each formation (overburden and reservoir).

[0139] Lateral variation of fracture density in each caprock and reservoir.

[0140] • Combination of vertical and lateral trends in fracture density in each caprock and reservoir.

[0141] • Fracture orientation, fracture length, fracture pore size, and permeability in each caprock and reservoir.

[0142] • Identify the permeable regions (reservoir size) in each caprock and reservoir.

[0143] • Expand the entire fracture group in each overburden and reservoir.

[0144] • Interpreting faults from core samples, and the alteration that causes cementation and grain size reduction during faulting, is visually difficult to detect because sedimentary processes in carbonate systems can produce structures and grain configurations that look very similar, with little or no color change. This problem is exacerbated in highly deviated wells (lateral wells) because the intersection of horizontal wells with steep fault and fracture surfaces means that fractures and faults exhibit very similar damage in the core sample to drilling-induced damage.

[0145] These problems are overcome by combining the interpretation of core CT scans with high-resolution borehole image data (such as image logging). Core CT scans reveal density variations associated with fracturing and faulting, while image logging reveals resistivity variations. The combination of all three factors allows for comparison of different physical properties of the rock, rather than just visual inspection. Furthermore, by combining helical CT scan data with borehole image logging, utilizing azimuth data from the borehole image logging, very high-resolution picking and localization of fracture and fault surfaces can be performed directly from the core. If the sampled formation is indeed affected, this data combination reveals the presence of faults.

[0146] • Structural core description of the entire core and / or slab core to calibrate image logging and CT scan observations, and fully characterize fracture density in fault zones, using image logging and core data to characterize and study faults in detail.

[0147] In addition, characterization of possible cementation within fault zones is crucial to fully understand the nature of the faults, estimate their significance and number, and confirm the existence of vertical connections between different strata.

[0148] The fundamental faulting behavior of caprocks and reservoirs, and their reactivation, and their impact on local oil and gas accumulation areas, require high-precision fault and fracture identification.

[0149] • Fault rock characteristics from reservoir and overburden units, to comprehensively assess their geological control over permeability.

[0150] Preliminary results indicate that factors such as fault displacement, reservoir Young's modulus, and stress history play important roles in controlling fault rock permeability. This project has effectively assessed the dependence of these parameters.

[0151] • An algorithm for evaluating the impact of fault segments on fluid flow within the studied reservoir.

[0152] Even during shallow burial, the influence of faults on high-porosity profiles, relative to low-porosity profiles, can impede fluid flow, thus isolating the reservoir. Therefore, the permeability of the deformation zone to be measured within a high-porosity fault is reduced. Consequently, the effects on low-porosity zones differ from those on high-porosity zones, affecting fluid flow.

[0153] The results indicate that cementation is the primary reason why faults may act as obstructors. Therefore, it is necessary to investigate the impact of compaction on the reactivation of fault segments.

[0154] • The influence of fractured fault segments and cemented breccia on permeability and fracture propagation, and whether this can be used to delineate fracture intervals within the deformation zone.

[0155] • Which crack groups can evolve, initiate, and expand due to production or even injection.

[0156] Low-displacement faults occurring in overburden and reservoirs are typically formed by expanding breccia that acts as conduits. However, the breccia fragments undergo fragmentation and deformation, increasing their ejection and leading to the formation of flow barriers.

[0157] • The overall trend of low-strength reservoirs deforming in a ductile compaction manner, and low-porosity and high-porosity sections initially fracturing in an expansive brittle manner, is similar to the ductile-to-brittle transition that occurs once it occurs and its impact on compaction.

[0158] A key objective is to identify the key controls (i.e., stress, strength, porosity, etc.) that enable the transition from ductile to brittle.

[0159] • The geomechanical properties of fault-bounded carbonate reservoirs and the correlation between apparent preconsolidation pressure (i.e., yield point under hydrostatic conditions) and porosity; a key objective is to understand the control over this relationship.

[0160] Crucially, the evidence for fault segmentation related to compaction must be rigorously evaluated, and if reactivation is a localized phenomenon and the fluid dynamics causing differences in cross-fault fluid contact, compaction, even across such faults, may not have a significant impact on production.

[0161] Faulted rocks can prevent the propagation of open fractures, which may reduce the connectivity within the reservoir. The impact on compaction can be predicted by combining core observations with fracture passivation theory.

[0162] • An algorithm for assessing the impact of fault segment reactivation on reservoir fluid flow, combined with a rock physics model derived from core samples obtained from BHI on fault rock samples, to generate an equation for calculating reservoir transmissibility factors (see [link]). Figure 17 ).

[0163] • Evaluate the dynamic properties (conductivity and pore size) of the fracture corridor, which in turn allows for the calculation of fracture porosity and permeability tensors (see...). Figure 18 ).

[0164] Finally, in this stage, the impact of natural fractures on reservoir deformation is assessed (see...). Figure 19 Impact on potential penetration rate (see) Figure 20 ), fault slip analysis (see Figure 21 The effects of fracture and cracking on sliding stress were also determined (see [reference]). Figure 22 ).

[0165] In a preferred embodiment, the structural model includes information about tectonic stresses in the geological region.

[0166] In a preferred embodiment, the geological basin model and the geomechanical model are combined with the structural model to generate strain maps.

[0167] In a preferred embodiment, the structural model is combined with the comprehensive model.

[0168] • Use the outputs of all previous stages to synthesize a model that includes a three-dimensional geomechanical model, and construct a three-dimensional geomechanical model by means of the distribution of mechanical properties such as Young's modulus, Poisson's ratio, friction angle, UCS (unconfined compressive strength), and tensile strength of reservoir overburden and boundary rock layers.

[0169] • The three-dimensional pre-production stress state was calculated, including the magnitude and direction of the total vertical stress, the maximum horizontal stress, and the minimum horizontal stress.

[0170] • A comprehensive model is established by combining individual reservoir models using a three-dimensional mesh structure and embedding overburden, underburden, and lateral overburden layers.

[0171] • A suitable 3D mesh was constructed based on the previous model and using pressure data from the dynamic reservoir model.

[0172] In a preferred embodiment, generating the comprehensive model further includes at least one of the following steps:

[0173] a. Overall three-dimensional mechanical properties;

[0174] b. Mechanical properties and stress model;

[0175] c. Pore pressure is prepared at a selected time step;

[0176] d. Three-dimensional pre-production stress modeling and calibration.

[0177] For the overall three-dimensional mechanical properties, this task is mainly accomplished by combining one-dimensional geomechanical models with three-dimensional seismic-related properties and attributes.

[0178] The main inputs to the overall three-dimensional mechanical properties are a one-dimensional geomechanical model and seismic data (post-stack earthquakes or pre-stack earthquake inversions).

[0179] • A total of three-dimensional mechanical properties driven by reservoir porosity and an amplified one-dimensional geomechanical model, along with co-krigged seismic data (acoustic impedance, Vp, Vs, etc.). This option is used to fill the mechanical properties in a three-dimensional geomechanical grid when pre-stack seismic inversion is unavailable but post-stack results (such as acoustic impedance and velocity cubes for the entire field) are available, for example in Abu Dhabi. Key steps include:

[0180] • Establish the relationship between mechanical properties and reservoir porosity. If necessary, develop different correlations for each reservoir based on one-dimensional geomechanical model data and laboratory measurement data;

[0181] • Based on the developed correlations, fill in the three-dimensional mechanical properties of all reservoirs;

[0182] • Based on the expanded mechanical properties of the one-dimensional geomechanical model and appropriate seismic attributes, the mechanical properties in non-reservoir grid cells are filled using the co-Kriging method.

[0183] • The distribution of mechanical properties should be consistent with the correlation between mechanical properties and porosity. For example, Young's modulus increases as porosity decreases. Secondly, the mechanical properties of the one-dimensional geomechanical model were compared with the three-dimensional mechanical properties along the well trajectory of the one-dimensional geomechanical model.

[0184] • For a relatively representative mechanical property model, the three-dimensional mechanical properties should be matched with the one-dimensional geomechanical model along the well trajectory.

[0185] • Three-dimensional mechanical properties driven by seismic inversion. Based on seismic inversion data, a one-dimensional geomechanical model, and laboratory core test data, and using appropriate seismic inversion cube data including overburden, the three-dimensional distribution of spatially heterogeneous rock mechanical properties within the entire geomechanical model was obtained. A typical workflow for filling in the three-dimensional mechanical properties includes the following key steps:

[0186] • Perform QC on the seismic inversion cube using a one-dimensional relation. If any mismatch is found, fine-tune the quality of the seismic inversion data and well data until at least a reasonable match is achieved.

[0187] • Calculate the dynamic Young's modulus from seismic inversion data.

[0188] • Based on the correlation of mechanical properties developed using one-dimensional geomechanical models and core test data measured in the laboratory, mechanical properties are filled into three-dimensional geomechanical models.

[0189] •QC three-dimensional mechanical properties.

[0190] • The one-dimensional geomechanical model was compared with the three-dimensional mechanical properties extracted along the well trajectory. If any obvious mismatch was found, the correlation of the mechanical properties was fine-tuned until at least a reasonable match was achieved.

[0191] • Blind logging was also performed on a select few wells to further ensure that the properties of the logging-derived data matched the one-dimensional and three-dimensional models.

[0192] Mechanical properties and stress model

[0193] In geomechanical models, the concept of "equivalent material" is used to simulate the deformation behavior of fault elements. The normal and shear stiffness properties of the fault are estimated based on the Young's modulus of the surrounding intact rock, which is used to define the elastic deformation behavior of the fault elements. The orientation of the fault plane at each grid element provides the specific direction of fault shearing and propagation. "Discontinuity modeling" is used to assign "equivalent" stiffness properties to elements intersecting the fault plane to capture their deformation and failure behavior. Mathematically, equivalent properties are calculated by combining the properties of intact rock and faults (joints) using constitutive theory. It is assumed that relative motion exists between elements along the fault plane due to their different mechanical properties from surrounding elements.

[0194] • Fault mesh elements are treated as fault elements with stiffness characterized by normal stiffness and shear stiffness. Faults are modeled as embedded fault planes within intersecting mesh elements. The elastic deformation behavior of the simulated fault elements is jointly determined by the elastic properties of the intact rock and the fault planes.

[0195] • In the direction perpendicular to the fault plane, both the fault plane and the intact rock are under the same stress. Therefore, the normal strain of the fault element can be expressed as:

[0196]

[0197] Where σ is the normal stress acting on the fault element perpendicular to the surface of the fault plane, and E equiv It is the equivalent Young's modulus, E intact It is the Young's modulus of intact rock, E fault It is the Young's modulus of the fault. E fault The distance (S) between the fault and the fault plane within the unit and the normal stiffness (K) of the fault plane n (Related to)

[0198] Then you can export:

[0199]

[0200] Assume E equiv =E intact *a (where a is the sensitivity analysis parameter (ranging from 0 to 1)), then K n It can be calculated in the following ways:

[0201]

[0202] K s Shear stiffness is the shear stiffness of a fault plane, used to limit the elastic shear deformation of fault elements under shear stress. The shear stiffness of a fault plane is related to the lithology of the intact rock, the fault shear displacement, and the properties of the fault gouge (if any). A typical value for fault shear stiffness is assumed to be the normal stiffness K. nThe value is 40%-60%. The cohesion of a fault typically has a very low value or is zero to reflect the typical mechanical behavior of discontinuities such as faults.

[0203] Pore ​​pressure preparation is performed at a selected time step.

[0204] For reservoirs, production scenarios in all reservoir models for different reservoirs range from the earliest 1960 (Thamama B) to the latest 2017 (HB1 and Thamama A). Production end dates are 2023 (Thamama G), 2051 (Thamama C), 2058 (Thamama H), and 2117 (Thamama A).

[0205] • To better understand the timing of rock deformation and potential geomechanical issues, a time step scheme is needed to determine the optimal time to incorporate the depletion effect in coupled simulations.

[0206] • The time step is the point in time at which stress analysis is performed, taking into account the effects of pressure, and providing a suitable point in time for verifying events relevant to geomechanics.

[0207] • To determine the optimal timing for these time steps, average field pressure values ​​were plotted based on production time to identify periods of greatest pressure variation.

[0208] The detailed process of selecting the time step can be summarized as follows:

[0209] For all reservoirs (each site), a comprehensive analysis of the site-averaged pressure for all reservoirs was performed.

[0210] o combines dates for time steps by considering all dates from unidirectional and bidirectional coupling.

[0211] After extracting the pressure at the selected time step, the pressure is exported from the Eclipse model and assigned to the corresponding reservoir grids previously built at the respective time steps:

[0212] • For all reservoirs (each site), the pressure at the selected time step is mapped to the geomechanical model of each reservoir grid;

[0213] • For non-reservoir grids, the pressure gradient remains constant.

[0214] 3D pre-production stress modeling and calibration

[0215] • Perform 3D pre-production stress modeling and calibration on the embedded 3D geomechanical model.

[0216] • Export the embedded model to the finite element geomechanics simulator. Use the pore pressure in the pre-production reservoir model as the initial pressure distribution inside the reservoir.

[0217] As mentioned above, the pore pressure distribution in non-reservoir and surrounding strata is based on pore pressure data from a one-dimensional geomechanical model.

[0218] • The total vertical stress in the three-dimensional model is calculated using a three-dimensional density cube in an embedded three-dimensional geomechanical model.

[0219] • Regional stresses based on a one-dimensional geomechanical model and consistent with the regional geological background are applied to the model boundary.

[0220] Stress balancing is then performed on the model to achieve initial static stress balancing for pre-production.

[0221] Since the mechanical properties within a formation are unlikely to be uniform, the equilibrium stress state reflects these variations in mechanical properties, including the effects of fault presence.

[0222] • A series of parametric steps were performed to fine-tune the predicted initial pre-production stress until:

[0223] (a) The stress state calculated in the three-dimensional geomechanical model is consistent with the stress in the one-dimensional geomechanical model.

[0224] (b) Calculated mud weight windows for available and selected offset wells revealed consistency between the three-dimensional geomechanical model and the one-dimensional geomechanical model.

[0225] Once the geostress profile and mud weight between the three-dimensional geomechanical model and the one-dimensional geomechanical model are consistent and matched, the calculated three-dimensional initial stress state not only represents the geostress state on the existing well trajectory, but also the geostress state between wells.

[0226] The unique three-dimensional stress generation and calibration technique proposed in this invention takes into account the balance of the entire three-dimensional model and can predict stress rotation near the fault (see...). Figure 23 ), and other discontinuities, such as cracks (see Figure 24 ), bedding planes, etc.

[0227] • The Mohr-Coulomb and Cap models were used to identify the locations of shear / tension and pore collapse failures in the field. By coupling geomechanical numerical simulations, the timing and location of failure could be identified based on the predicted failure index (plastic strain) in the field.

[0228] The stability of a fault is controlled by the corresponding stress state, fault properties (magnitude, dip angle, and dip direction), and fault strength parameters (see [reference]). Figure 23 and Figure 24 The slip potential of all faults simulated in the three-dimensional geomechanical model was calculated at the current and future time steps. The slip potential is represented by values ​​between zero and one. A low slip potential indicates a lower risk of fault reactivation. When the slip potential of a fault approaches 1, relatively small changes in the stress state can potentially reactivate the fault. When the slip potential equals 1, the fault is in a critical stress state.

[0229] In a preferred embodiment, oil and gas accumulation is predicted based on the output received from the steps mentioned above.

[0230] Oil and gas accumulation

[0231] Based on the simulation results from the above steps, oil and gas accumulation can be obtained:

[0232] For example, a three-dimensional porous elastic-brittle finite element (FE) model of the Abu Dhabi region produced a wide variety of output data, such as principal stress vectors whose magnitudes have been normalized by the overburden stress (see [link to relevant documentation]). Figure 25 ).

[0233] Next, the mean stress and shear stress, as well as the full Cartesian strain and stress tensor, are output for analysis (see [link]). Figure 26 ).

[0234] The current stress state and reservoir deformation will continue to change during future production. To assess the impact of stress changes on reservoir deformation during future production, coupled reservoir simulations were performed from the current stress state to the oilfield lifetime.

[0235] • At each predetermined time step, reservoir pressure variations in the reservoir model are used to calculate stress variations in the reservoir and surrounding formations (see...). Figure 27 ).

[0236] • By using calculated strain and comparing these strain maps with oil fields and oil and gas accumulations, it was found that they matched.

[0237] Therefore, the workflow of this invention is a good workflow for predicting oil and gas accumulation.

[0238] • It has been found that there is a certain trend of oil and gas accumulation, hence the name "oil and gas belt".

[0239] In a preferred embodiment, the step of generating the strain map includes the following steps:

[0240] a. Modeling of overburden stress in geological regions;

[0241] b. Modeling of effective stress in geological regions;

[0242] c. Modeling of pore stress in geological regions.

[0243] In a preferred embodiment, the strain map indicates areas of high and low strain. In a preferred embodiment, the prediction of hydrocarbon accumulation includes the delineation of trapped hydrocarbon areas and the prediction of hydrocarbon migration paths. Furthermore, the aforementioned problems can be solved at least in part by a map indicating hydrocarbon accumulation, wherein the map is obtained through a prediction method based on one of the aforementioned features. In this document, the term "map" should be understood broadly as an appropriate representation of information provided in a user-perceptible manner, including but not limited to one or more graphical 2D and 3D representations. Therefore, visualized hydrocarbon accumulation areas can enable and / or facilitate exploration and oilfield development plans.

[0244] Furthermore, the above-mentioned problems can be solved at least in part by a computer program product that includes instructions that, when executed by a computer, cause the computer to perform the steps of the above-described method.

[0245] Furthermore, the aforementioned problem can be solved at least in part by a computer-readable storage medium including instructions that, when executed by a computer, cause the computer to perform the steps of the aforementioned method.

[0246] Furthermore, the aforementioned problems can be solved, at least in part, by a data processing system that includes means for performing the above-described method steps. Attached Figure Description

[0247] Preferred embodiments of the invention are disclosed below with reference to the accompanying drawings, wherein:

[0248] Figure 1 The present invention illustrates a workflow for creating strain maps, oil and gas accumulation, and bands;

[0249] Figure 2 The AC illustrates a geological model according to the invention, in which any deposited layer undergoes two processes: compaction and tectonics;

[0250] Figure 3 The AC illustrates porosity modeling according to the present invention;

[0251] Figure 4 The AD illustrates the application of the porosity model according to the invention in a formation;

[0252] Figure 5 A three-dimensional porosity model according to the present invention is shown;

[0253] Figure 6 AB shows the calibration pressure model according to the present invention;

[0254] Figure 7 The AD illustrates an example of a pressure model in a formation according to the present invention;

[0255] Figure 8 A three-dimensional pressure model according to the present invention is shown;

[0256] Figure 9 The AD diagram illustrates the overpressure results of a formation according to the present invention;

[0257] Figure 10 Figure AB shows the overpressure and permeability diagrams according to the present invention;

[0258] Figure 11 The dependence of density on the range of seismic angles is shown to estimate the characteristics of the layers;

[0259] Figure 12 AC illustrates the mechanical properties based on porosity correlation derived from core results in the workflow of the one-dimensional geomechanical model according to the present invention.

[0260] Figure 13 An example of a one-dimensional geomechanical model according to the present invention is shown;

[0261] Figure 14 The AE illustrates a mapping of mechanical parameters across Abu Dhabi according to the present invention;

[0262] Figure 15 An example of borehole image logging according to the present invention is shown;

[0263] Figure 16 The AC diagram illustrates the extraction of the seismic discontinuity plane (SDP) according to the present invention: the analysis and input of the DFN;

[0264] Figure 17 AB illustrates a fault corridor in a field according to the present invention. Figure 17 A) and the reactivation of some fault segments within the corridor ( Figure 17 B);

[0265] Figure 18 The AE illustrates the dynamic characteristics (conductivity and pore size) of the fracture corridor according to the invention, resulting in fracture porosity and permeability tensors;

[0266] Figure 19 The AF illustrates the effect of natural fractures in a formation on reservoir deformation according to the present invention;

[0267] Figure 20 The AF illustrates the effect of natural fractures on potential permeability in a reservoir section according to the present invention;

[0268] Figure 21 AB illustrates the effect of natural fractures on fault slip analysis according to the present invention;

[0269] Figure 22The AF diagram illustrates the effect of the fault on the stress direction according to the present invention;

[0270] Figure 23 A shear stress diagram relative to structural stress is shown according to the present invention;

[0271] Figure 24 Stress rotation near the fault is shown according to the invention;

[0272] Figure 25 A finite element model of the Abu Dhabi region normalized by overburden stress according to the present invention is shown;

[0273] Figure 26 A graph showing the mean stress and shear stress according to the present invention is shown;

[0274] Figure 27 A diagram illustrating oil and gas accumulation according to the present invention is shown. Detailed Implementation

[0275] Figure 1 A workflow for creating strain maps, oil and gas accumulation, and bands according to the present invention is shown. Here, Figure 1 An overview of the steps that can be used to generate the corresponding model is provided. Specifically, Figure 1 The stratigraphic horizons (surface) and faults are shown as interpreted from seismic data and derived from isopyre maps (see blue boxes numbered 1 to 12). Furthermore, Figure 1 The steps related to the seismic inversion process are shown (see the orange boxes in numbers 13 and 14). Furthermore, Figure 1 The steps for generating the one-dimensional geomechanical model are shown (see the purple boxes in numbers 15-20), and the three-dimensional model is shown in the dark blue box in number 21. Furthermore, Figure 1 The steps for 3D static and dynamic modeling are shown (see the green boxes for numbers 22-25 and the red boxes for numbers 26-29). Furthermore, Figure 1 The steps associated with generating the integrated model up to the strain diagram are shown; oil and gas accumulation and oil and gas bands (see yellow boxes in numbers 30 to 35).

[0276] Figure 2 The AC model illustrates a geological model according to the invention, in which any deposited layer undergoes two processes: compaction and tectonics. This involves... Figure 1 Steps 1-12 in the process. Figure 2 Figure A shows the model's return to the oldest strata. The simulation process begins with the decomposition of the stratigraphic layers and then redepositions each older stratum up to the present. Figure 2 B and Figure 2(C). Parameters such as porosity and pore pressure were calculated at each geological time stage. These calculations were controlled by lithological parameters for each layer. The simulation results were analyzed and compared with existing well data such as porosity and formation pore pressure. Calibration was required when the calculated outputs did not match the well data. The initial model parameters needed to be modified, and this modification was performed during the model building step. Once the modification was complete, the model needed to be resimulated. The outputs of the modified model should match the well data. Here, the lithological parameters were modified to ensure good matching between the porosity and pore pressure outputs and the well data.

[0277] Figure 3 The AC diagram illustrates porosity modeling according to the present invention. This involves... Figure 1 Steps 4-10 in the process. Figure 3 A and Figure 3 Figure B shows the porosity and modeling pressure at different depths. The compaction curves of the lithological layer were calibrated using the porosity-effective stress relationship. Figure 3 C shows the calibrated compaction curve and the default compaction curve.

[0278] Figure 4 A to Figure 4 Figure D illustrates the application of the porosity model according to the invention to a formation. This involves... Figure 1 Steps 7-12 in the process. The simulated porosity model can predict the porosity of each formation (see...). Figure 4 The porosity is calculated based on compaction curves, which are formation-specific. This method also captures the spatial variation of porosity across the entire formation. The porosity of a given geological region is... Figure 4 A displays today's time. Figure 4 The time point shown in C is 95 million years ago. Figure 4 B shows the porosity at well locations, indicated by "A", from approximately 100 million years ago to the present (see Figure 1). Figure 4 (A) As can be seen from the figure, porosity decreases over time. Figure 4 Figure D shows a burial map of different geological layers at different depths from 95 million years ago to the present, with porosity superimposed at the location of well "A" (see Figure D). Figure 4 (A).

[0279] Figure 5 A three-dimensional porosity model according to the present invention is shown. This involves Figure 1 Steps 10-12 in the process. Based on the results, such as Figure 4 As shown, the porosity distribution within the sequence stratigraphy of rocks was predicted and calibrated using real data from current laboratory tests.

[0280] Figure 6 A and Figure 6 B illustrates the calibration pressure model according to the present invention. This involves Figure 1 Steps 1-12 in the process. Figure 6 Figure A illustrates this example, where three pairs of well logging permeability-porosity values ​​are plotted for the Laffan formation as an example. By reducing the permeability value corresponding to its porosity, fluid flow is restricted, and pore pressure at and below the formation increases. Figure 6 Figure B shows the pressure simulation of hydrostatic pressure, static lithological pressure and pore pressure at different depths of the geological strata at well A.

[0281] Figure 7 A to Figure 7 D illustrates an example of a pressure model in a formation according to the present invention. This involves Figure 1 Steps 1-12 in the model. Formation pore pressure exhibits a well-defined spatial pressure distribution, and its evolution reflects geological events captured during the establishment of the structural model. For a given geological region, the pore pressure is as follows: Figure 7 The three-dimensional model in A is shown. Figure 7 B shows from Figure 7 The pressure created by the model in A is a layer (layer). Figure 7 C shows the pressure variation over time created from a three-dimensional model of a well (A) location. Figure 7 D shows the burial map of different geological layers at different depths, where the pore pressure is superimposed at the location of well A (see Figure 1). Figure 7 (A).

[0282] Figure 8 A three-dimensional pressure model according to the present invention is shown. This relates to... Figure 1 Steps 1-12 in the text. Here, as... Figure 7 The results shown are the simulation and prediction values ​​for each stratigraphic layer.

[0283] Figure 9 A to Figure 9 D illustrates the overpressure result of a formation according to the present invention. This relates to... Figure 1 Steps 1-12 in the process. Overpressure in a given geological region. Figure 9 A displays today's time. Figure 9 The B layer is shown as an example (95 million years ago). Figure 9 C shows well A during different periods from 100 million years ago to the present (see Figure 1). Figure 9 The formation overpressure at location A). Figure 9 D shows overpressure superposition at well location "A" during different time periods from 100 million years ago to the present (see Figure 1). Figure 9A) Burial maps of different geological layers at different depths. Modeling overpressure is crucial, such as... Figure 9 As shown, the overpressure region observed in the simulation results is revealed. This clearly demonstrates that pressure increases with depth. The formation pressure network is crucial for predicting overpressure in the model. The connectivity of low-permeability formations influences the pressure system of adjacent formations. Formation properties allow pressure to shift via the movement of fluids within the formation, such as native water, from high-pressure areas to low-pressure areas.

[0284] Figure 10 A and Figure 10 Figure B shows the overpressure and permeability diagram according to the present invention. This relates to... Figure 1 Steps 1-12 in the diagram. These graphs are along... Figure 4 , Figure 7 and Figure 9 The area depicted is intercepted by the Y-to-Y' line, as shown in the image. Figure 10 As shown in B'. Here, Figure 10 Figure A shows the overpressure at different depths and corresponding layers along the Y to Y' line. Figure 10 Figure B shows the horizontal permeability at different depths and corresponding layers along the Y to Y' line. The corresponding arrows indicate the corresponding fluid flows. As previously mentioned, the properties of the formation allow pressure to be transferred through the movement of fluids within the formation, such as native water, from high-pressure areas to low-pressure areas. This can occur in... Figure 10 A and Figure 10 As seen in the overpressure model of layer B, which serves as an example formation, the overpressure in deeper sections of the formation is lower than that in shallower sections.

[0285] Figure 11 The dependence of density on the range of seismic angles is shown to estimate the characteristics of the layers. This involves Figure 1 Steps 13-14 in the process. Elastic parameters are created by following a workflow that depends on pre-stack seismic inversion.

[0286] Figure 12 The AC diagram illustrates the porosity-related mechanical properties derived from core logging results in the workflow of the one-dimensional geomechanical model according to the present invention; the results of the one-dimensional geomechanical model were calibrated using laboratory core measurements. This involves... Figure 1 Steps 13-14 and 15-21 in the text. Here, Figure 12 A shows the parameters created by pre-stack inversion, calibrated with the results of a one-dimensional geomechanical model (15-21). Figure 12 B shows the variation of Young's modulus in some layers. Figure 12 C1, 2, and 3 show the mechanical parameters at a layer as an example.

[0287] Figure 13An example of a one-dimensional geomechanical model according to the present invention is shown. This involves Figure 1 Steps 15-20 are described below. Here, the model is built using the Abu Dhabi oilfield as an example. The first line (NR.1) shows the depth. The second line (NR.2) shows the selected formation as an example. The third line (NR.3) shows Young's modulus (YME) and Poisson's ratio (PR). The fourth line (NR.4) shows the unconfined compressive strength (UCS), tensile strength (TSTR), and internal friction angle (FANG). The fifth line (NR.5) shows the stresses; the black curves represent vertical stress (sv), SHmax (maximum horizontal stress), and SHmin (minimum horizontal stress). The sixth line (NR.6) shows the wellbore stability results, showing the safe mud window and fracture gradient. The seventh line (NR.7) shows the unstable region, and the eighth line (NR.8) shows the wellbore diameter.

[0288] Figure 14 A to Figure 14 E illustrates a mapping of mechanical parameters across Abu Dhabi according to the present invention. This relates to... Figure 1 Steps 13-21 in the process. Here, using available well logging and core test data, rock elastic and strength property parameters of the overburden and reservoir sections are constructed for calibration. Well logging-derived elastic and rock strength property profiles are established using the most suitable correlation. Specifically, Figure 14 A indicates Young's modulus; Figure 14 B represents Poisson's ratio; Figure 14 C represents the unconfined compressive strength; Figure 14 D indicates the tensile strength; Figure 14 E indicates the minimum horizontal stress. The ellipses in each figure indicate A, B, C, D, E, and F, representing selected wells used to verify the mechanical parameters.

[0289] Figure 15 An example of borehole image logging according to the present invention is shown. This involves... Figure 1 Steps 18 and 26-29 in the diagram. The first pass (A) shows the minimum horizontal stress (SHMIN) that depends on the fracture; measured directly by experiment; the second pass (B) shows the conductivity; the third pass (C) shows the static image, and the fourth pass (D) shows CS: orientation and dip of the conductive fracture; DCF = LC: discontinuous conductive fracture, and SCF: secondary conductive fracture.

[0290] Figure 16 A to Figure 16 C illustrates crack and micro-fault modeling: analysis and input of the DFN according to the present invention. This involves... Figure 1 Steps 26-29 in the text. Specifically, Figure 16A shows crack detection: structural decomposition (seismic body properties). Figure 16 B shows the stratigraphy, fault interpretation, and natural fractures surrounding the BHI well. Figure 16 C illustrates the extraction of SDP (Seismic Discontinuity Plane Map): analysis and input of DFN.

[0291] Figure 17 A shows a fault corridor for an onshore oil field in Abu Dhabi; Figure 17 Figure B illustrates the reactivation of some fault segments within the corridor according to the invention. This involves... Figure 1 Steps 22-29 in the process.

[0292] Figure 18 A to Figure 18 E illustrates the dynamic properties (conductivity and pore size) of the fracture corridor according to the invention, resulting in fracture porosity and permeability tensors; this relates to Figure 1 Steps 22-29 in the text. Specifically, in Figure 18 In A, the crack pore size and connectivity from BHI are used to calibrate and validate the porosity model created from steps 1-12. Figure 18 B shows a rock physics model with saturation; Figure 18 C illustrates fluid contact in a reservoir as a common contact. Figure 18 D shows the result of the formula used in the volume calculation: HCV = pore volume × So, and Figure 18 E represents STOIIP = HCVo / Bg + (HCVg / Bg) × Rv. Abbreviations: STOIIP = Initial Appropriate Oil Reserve, the volume of oil in the pre-production reservoir; HCP = HC (oil and gas) initially replacing oil. GRV = Total Volume; NRF = Net Rock Volume; NPV = Net Pore Volume; HCPV = Oil and Gas Pore Volume; So = Oil Saturation, etc.

[0293] Figure 19 A to Figure 19 F illustrates the effect of natural fractures in a formation on reservoir deformation according to the present invention. This relates to... Figure 1 Steps 22-29 in the text. Specifically, Figure 19 A shows the shear strain without cracks. Figure 19 B represents the total strain (deformation) when a crack is present. Figure 19 The C value indicates not only the reservoir but also the volumetric strain due to the overburden. Figure 19 D indicates increased deformation around the fault. Figure 19 E represents the horizontal strain. Figure 19 F shows the deformation around the fault and crack on the horizontal plane.

[0294] Figure 20 A to Figure 20 F illustrates the effect of natural fractures on potential permeability in a reservoir section according to the present invention. This involves Figure 1 Steps 22-29 in the text. Specifically, Figure 20 A shows the volumetric compressibility in the absence of cracks. Figure 20 B shows the volumetric compressibility in the presence of cracks. Figure 20 C indicates shear capacity, and Figure 20 The value of D indicates the shear capacity around the fault and fracture. Figure 20 E shows the compressibility of a layer, and Figure 20 F shows the effects of more cracks and faults.

[0295] Figure 21 A and Figure 21 Figure B illustrates the fault slip potential analysis according to the present invention. This involves... Figure 1 Steps 26-29 in the text. Specifically, Figure 21 A shows the slip along the fault. Figure 21 B shows those cracks that contain potential slippage.

[0296] Figure 22 A to Figure 22 F illustrates the effect of the fault on the stress direction according to the present invention. This involves Figure 1 Steps 26-29 in the text. Specifically, Figure 22 A, Figure 22 B and Figure 22 C shows the stress analysis around the fault, which shows the total stress and eliminates stress bias. Figure 22 D, Figure 22 E and Figure 22 F shows the corresponding stress variation, which indicates the maximum and minimum horizontal stress.

[0297] Figure 23 A shear stress diagram relative to structural stress according to the invention is shown. This relates to... Figure 1 Steps 26-29 in the diagram clearly show the stress rotation around the main fault.

[0298] Figure 24 The stress rotation near the fault according to the invention is shown. This involves Figure 1 Steps 26-29 in the diagram. This shows stress rotation around some faults, while others do not.

[0299] Figure 25A finite element model of the Abu Dhabi region, normalized by overburden stress according to the present invention, is shown. This model illustrates all layers and horizons from the surface to the reservoir level. This model synthesizes all previous models. This involves... Figure 1 Steps 21 and 30 in the text.

[0300] Figure 26 A graph showing the mean stress and shear stress according to the invention is shown. This relates to... Figure 1 Step 32 in the diagram. This shows the shear stress in a layer as an example.

[0301] Figure 27 A diagram illustrating oil and gas accumulation according to the present invention is shown. This relates to... Figure 1 Steps 31-35 in the diagram. This figure illustrates hydrocarbon accumulation and the tendency for hydrocarbon belts to form along one direction. Hydrocarbon accumulation is associated with low-strain regions. Some of these show a strong trend, meaning they are tectonically related and are therefore named hydrocarbon belts.

Claims

1. A method for predicting oil and gas accumulation in a geological region, the method comprising the following steps: a. Generate a geological basin model, wherein the geological basin model includes the step of overpressure modeling of the geological region, wherein the overpressure is the overpressure of deeper strata, the modeling depends on the evolution of the primary water vector over geological time, and wherein the primary water vector depends on multiple lithological parameters and the capillary ingress pressure of adjacent model layers. b. Generating a geomechanical model, wherein generating the geomechanical model further includes at least one of the following steps: ba seismic inversion and detailed rock physical analysis including fluid substitution modeling; Pre-stack seismic data adjustment; bc pre-stack AVO simultaneous inversion; bd predicts mechanical properties based on the porosity correlation derived from core results; be used to generate a one-dimensional geomechanical model; Among them, the prediction of mechanical properties based on porosity correlation derived from core results also includes at least one of the following: The bf porosity cube is derived from the reservoir model; In tight units within caprock and segregated reservoir zones, the prediction of the mechanical properties is based on synergistic Kriging amplification logging; and The mechanical property profile of bh is derived from a one-dimensional geomechanical model; c. Generate a comprehensive model, wherein the comprehensive model combines the geological basin model and the geomechanical model; d. Generate strain maps based on the information obtained in steps a to c; e. Predict oil and gas accumulation from the strain diagram.

2. A method of predicting hydrocarbon accumulation in a geological region according to the preceding claim, wherein, The geological basin model further includes at least one of the following steps: a. Determine the stratigraphic level and faults; b. Recovery and re-mining to identify construction events; c. Porosity modeling; d. Stress modeling; e. Modeling the porosity-permeability relationship.

3. A method of predicting hydrocarbon accumulation in a geological region according to any of the preceding claims, wherein, The stress modeling process also includes at least one of the following steps: a. Calibration of the pore pressure model; b. Application of the pore pressure model in the geological region.

4. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The geological basin model includes mechanical stratigraphy.

5. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The geological basin model includes a permeability modeling step.

6. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The geological basin model further includes at least one of the following steps: a. Decomposition of sediments; b. Obtain the burial history of the geological area.

7. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, further comprising the step of creating a structural model, wherein, The method also includes the steps of estimating the three-dimensional static and dynamic states of the geomechanical model.

8. The method for predicting oil and gas accumulation in a geological region according to claim 1, including the steps of fault and fracture analysis.

9. The method for predicting oil and gas accumulation in a geological region according to claim 1, comprising the following steps: a. Generate discrete crack networks; b. Extend the discrete fracture network into a static geomechanical model.

10. The method of predicting hydrocarbon accumulation in a geologic region of any one of claims 7 to 9, wherein, The structural model includes information about tectonic stresses in the geological region.

11. The method of predicting hydrocarbon accumulation in a geologic region of any one of claims 7 to 9, wherein, The geological basin model and the geomechanical model are combined with the structural model to generate the strain diagram.

12. The method of predicting hydrocarbon accumulation in a geologic region of any one of claims 7-9, wherein, The structural model is combined with the comprehensive model.

13. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, Generating the integrated model further includes at least one of the following steps: a. Overall three-dimensional mechanical properties; b. Mechanical properties and stress model; c. Pore pressure is prepared at a selected time step; d. Three-dimensional pre-production stress modeling and calibration.

14. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The output received from steps a to d predicts the oil and gas accumulation.

15. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The steps for generating the strain diagram include the following: a. Modeling of the overburden stress in the geological region; b. Modeling of the effective stress in the geological region; c. Modeling of pore stress in the geological region.

16. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The strain map indicates high strain regions and low strain regions.

17. The method of predicting hydrocarbon accumulation in a geologic region of claim 1, wherein, The prediction of oil and gas accumulation includes the delineation of areas where oil and gas can be trapped and the prediction of oil and gas migration paths.

18. A map indicating hydrocarbon accumulation, wherein, The graph was obtained by the prediction method according to any one of claims 1 to 17.

19. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 1 to 17.

20. A computer-readable storage medium including instructions that, when executed by a computer, cause the computer to perform the steps of the method of claims 1 to 17.

21. A data processing system comprising means for performing the steps of the method of claims 1 to 17.

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