Saudi Arabian Oil Company

Peng Lu

Yupeng Li

Nikolaos Andreas Michael

Yufeng Cui

Pan Luo

Hongwen Zhao

Maher I Marhoon

Ming Lei Zhang

David Tang

Wei Wei

Shouxiang Mark Ma

Shuo Zhang

Khaled Arouri

Christopher Ayadiuno

Rainer Zuhlke

Yin Xu

Tiffany Dawn McAlpin

Wenbin Tan

Dave Cantrell

David Amendt

Libin Liu

Maolin Luo

Harald Karg

Susan Agar

Ibrahim Alfraih

A method for obtaining geological heterogeneity trends of a geological formation, including the steps: drilling wells (w, w, w, w, w) that penetrate the formation, acquiring well logs (log, log) for each well (w, w, w, w, w) as function of depth interVals (D) of the respectiVe well, determining a third degree tensor (T), where a z-dimension denotes the depths, a x-dimension denotes the well logs, and a y-dimension denotes the wells, extracting matrices (L, L, . . . , L) from the tensor (T), clustering the matrices (L, L, . . . , L) based on the characteristics of the corresponding well logs (log, log) to a clustering result matrix, aggregating the clustering result matrix to a cluster ensemble (π, π, . . . , π), and spatial partitioning the cluster ensemble (π, π, . . . , π) to a map that shows the geological heterogeneity trends associated with cluster types of the wells (w, w, w, w, w).

Method for obtaining geological heterogeneity trends of a geological formation

A method for hydrocarbon production from a carbonate formation includes receiving a carbonate texture description of a drill cutting. A pre-defined grain fraction is assigned to the drill cutting based on the texture description. The pre-defined grain fraction is one of a set of pre-defined grain fractions and each pre-defined grain fraction of the set of pre-defined grain fractions corresponds to a respective one the set of carbonate texture descriptions. Based on the pre-defined grain fraction assigned to the drill cutting and a depth of the wellbore from which the drill cutting is taken, an estimated porosity of the drill cutting is determined using an algebraic equation, coefficients for which are determined by regression analysis using reference rock samples that have been assigned pre-defined grain fractions from the same set of pre-defined grain fractions.

Hydrocarbon exploration and production using porosity variation prediction based on carbonate texture

Systems and methods for identifying natural hydrogen sweet spots in a subsurface formation include obtaining rock samples from the subsurface formation; performing pyrolysis of the rock samples to determine an amount of hydrogen generated in the rock samples; determining kinetic parameters based on the performed pyrolysis and a thermal history of the subsurface formation; simulating hydrogen generation in the subsurface formation based on the determined kinetic parameters and the thermal history to predict target maturity data for hydrogen richness in the subsurface formation; measuring total organic content and rock sample maturity data for the rock samples; and identifying hydrogen sweet spots in the subsurface formation based on the target maturity data, the total organic content, and the measured rock sample maturity data.

Identifying hydrogen sweet spots in subsurface formations

Systems and methods include a method for training machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). Training sample data is generated. Training sample cases are executed using the training sample data. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

Proxy modeling workflow and program for reactive transport modeling

Methods of the present disclosure may include obtaining an origin fluid having a calcium concentration of about 1.5 M or greater. A treatment fluid may be created by introducing an amount of slaked lime to the origin fluid to change a pH of the resulting treatment fluid to about 11 or greater. The treatment fluid may be introduced into a subterranean formation containing a formation gas comprising natural gas and carbon dioxide. The calcium in the treatment fluid may be allowed to react with the carbon dioxide within the subterranean formation to form calcite. The treated formation gas may be removed from the subterranean formation after the formation of calcite, wherein the concentration of carbon dioxide in the treated formation gas is lower than the formation gas before it was contacted with the treatment fluid.

Methods for removing carbon dioxide from natural gas

Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.

Random selection of observation cells for proxy modeling of reactive transport modeling

A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

Method and system for diagenesis-based rock classification

Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

User interface for proxy modeling of reactive transport modeling

Methods of estimating permeability of reservoir rocks using mercury injection capillary pressure can include: receiving mercury injection capillary pressure test data and porosity data for a core sample; determining a fractal dimension (D) for the core sample based on the received mercury injection capillary pressure test data for the core sample; determining a pore throat radius (R) for the core sample; determining a composite parameter (&#x3b2;) for the core sample where and estimating permeability (K) of the core sample based on a relationship of ln(K) as a function of ln(&#x3b2;) determined by performing a regression analysis data from other core samples from the reservoir.

Estimating permeability of reservoir rocks using mercury injection capillary pressure

A method of rock typing includes obtaining mercury injection capillary pressure (MICP) data regarding a region of interest. A distance matrix is computed for distributions determined from the MICP data using a statistical distance metric. A cluster tree of the distributions is generated using the distance matrix. The cluster tree is adjusted based on a petrographic characteristic to produce an adjusted cluster tree, which is used to determine a pore structure types of the region of interest.

System and method of typing heterogeneous reservoir rocks

Display screen with model training graphical user interface

Embodiments provide a method for evaluating a hydrocarbon-bearing formation. The method includes the step of identifying a hydrocarbon sweet spot. The identifying step includes determining a carbon dioxide content of a gas sample retrieved from the hydrocarbon-bearing formation. The identifying step includes determining an isotopic signature of carbon dioxide of the gas sample. The carbon dioxide content of the gas sample retrieved from the hydrocarbon sweet spot can have a mole percentage ranging from 9 percent to 20 percent. The isotopic signature of carbon dioxide of the gas sample retrieved from the hydrocarbon sweet spot can have a &#x3b4;C value greater than &#x2212;10 per mil. The identifying step can further include determining a cutoff range of the carbon dioxide content corresponding to the hydrocarbon sweet spot. The cutoff range can have a mole percentage ranging from 9 percent to 20 percent. The identifying step can further include obtaining a gas flow rate of the hydrocarbon-bearing formation. The gas flow rate can be greater than 10 million standard cubic feet per day.

Identifying hydrocarbon sweet spots using carbon dioxide geochemistry

Petrographic data of a carbonate reservoir in a subterranean region is received. Mud content of the carbonate reservoir is determined based on the petrographic data. A depositional porosity of the carbonate reservoir is computed based on the mud content. An amount of cementation of the carbonate reservoir is determined. A porosity loss by compaction of the carbonate reservoir is determined based on the mud content and the amount of cementation of the carbonate reservoir. A post-compaction porosity of the carbonate reservoir is computed based on the depositional porosity, the mud content, the amount of cementation, and the porosity loss by compaction.

Predicting carbonate porosity based on petrographic data

Apparatus and method for in-situ stabilization of unconsolidated sediment in core samples are disclosed. The core sampling apparatus includes a corer having an inner wall, an outer wall, and a plurality of impregnation tubes disposed between the inner and the outer wall, wherein the impregnation tubes are parallel to a central axis of the corer. The method for sampling a core includes extracting a core sample using a corer, and in-situ stabilizing unconsolidated sediment in the core sample within the corer by impregnating the core sample with a resin. The resin is supplied through a plurality of impregnation tubes disposed between the walls of the corer.

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Dammam, Saudi Arabia

Peng Lu