Abstract: Method for modelling a reservoir including receiving a data set relating to one or more wells within a reservoir, processing the data set to identify one or more fractures extending from the one or more wells into the reservoir, categorizing the one or more fractures as principal fractures or secondary fractures, identifying a near well high permeability region for each of the one or more wells, generating a gridded fracture plane of the one or more wells, determining a fracture permeability region, assembling the gridded fracture plane, the fracture permeability regions, and the near well high permeability region for each of the one or more wells, and generating a three-dimensional reservoir model based on the assembled gridded fracture plane for each of the plurality of predetermined depths, the fracture permeability regions, and the near well high permeability region for each of the one or more wells.

Abstract: A method of reducing gas flaring through modelling of reservoir behavior using a method of optimizing oil production from one or more well(s) in a reservoir, the method providing a model of the well, inputting well data for a one or more well(s) into the model, the well data selected from geological layers, reservoir properties, fracturing data, completion data, permeability data, geochemistry, and combinations thereof. Inputting historical production data from one or more well(s) into the model, the historical data selected from PVT data, BHP, oil production rates, gas production rates and water production rates, or combinations thereof. Controlling the model to match one or more parameters selected from production rates, gas to oil ratio (GOR), bottom hole pressure (BHP), cumulative oil production (COP), or a combination thereof in a probabilistic manner to obtain a plurality of historical models.

Abstract: Method for modelling a reservoir including receiving a data set relating to one or more wells within a reservoir, processing the data set to identify one or more fractures extending from the one or more wells into the reservoir, categorizing the one or more fractures as principal fractures or secondary fractures, identifying a near well high permeability region for each of the one or more wells, generating a gridded fracture plane of the one or more wells, determining a fracture permeability region, assembling the gridded fracture plane, the fracture permeability regions, and the near well high permeability region for each of the one or more wells, and generating a three-dimensional reservoir model based on the assembled gridded fracture plane for each of the plurality of predetermined depths, the fracture permeability regions, and the near well high permeability region for each of the one or more wells.

Abstract: The invention relates to a method for history matching a facies geostatistical model using the ensemble Kalman filter (EnKF) technique. The EnKF is not normally appropriate for discontinuous facies models such as multiple point simulation (MPS). In the method of the invention, an ensemble of realizations are generated and then uniform vectors on which those realizations are based are transformed to Gaussian vectors before applying the EnKF to the Gaussian vectors directly. The updated Gaussian vectors are then transformed back to uniform vectors which are used to update the realizations. The uniform vectors may be vectors on which the realizations are based directly; alternatively each realization may be based on a plurality of uniform vectors linearly combined with combination coefficients. In this case each realization is associated with a uniform vector made up from the combination coefficients, and the combination coefficient vector is then transformed to Gaussian and updated using EnKF.

Abstract: A multiple point simulation technique for generating a model realization of a subterranean formation having different facies is described which uses a non-stationary training image which reflects facies spatial trends across the formation. The realization is formed by sequentially populating each cell; a facies pattern of neighboring cells is identified for each cell in the model grid, and then corresponding facies patterns identified in the training image. The probability of a target cell in the model grid having a given facies is calculated based on the proportion of occurrences of the corresponding facies pattern where the central cell has that facies. The contribution of each corresponding facies pattern occurrence in the training image to this proportion or probability is weighted according to the distance between its central cell and the training image cell corresponding in location to the target cell in the model grid.

Abstract: The present disclosure describes a method that improves the long-range geobody continuity in Multiple Point Statistical methods, wherein the coarsest multi-grid level cells are simulated in a regular path, and the subsequent level cells are simulated in a random path as usual. The method is general and is applicable to different cases: such as hard data conditioning, soft data conditioning, non-stationarity modeling, 2 or more than 2 types of facies modeling, and 2D and 3D modeling. The method is particularly useful in reservoir modeling, especially for the channelized systems, but can be generally applied to other geological environments.

Abstract: Estimating in-situ stress of an interval having drilling response data is described. Estimating involves obtaining drilling response data of a data rich interval with available data. Estimating relative rock strength as a composite value that includes in-situ stress and rock strength. Estimating a Poisson's ratio from the relative rock strength. Generating a stress model that includes uniaxial strain model using the Poisson's ratio. Verifying the stress model with the available data. Applying the stress models in a non-data rich interval.

Abstract: The present disclosure describes a method that improves the long-range geobody continuity in Multiple Point Statistical methods, wherein the coarsest multi-grid level cells are simulated in a regular path, and the subsequent level cells are simulated in a random path as usual. The method is general and is applicable to different cases: such as hard data conditioning, soft data conditioning, non-stationarity modeling, 2 or more than 2 types of facies modeling, and 2D and 3D modeling. The method is particularly useful in reservoir modeling, especially for the channelized systems, but can be generally applied to other geological environments.

Abstract: A method of computer modeling a reservoir using multiple-point statistics from non-stationary training images is provided.

Abstract: A method for creating a modified realization of a geostatistical model of a subterranean hydrocarbon reservoir is described, which may be used in a history matching process. The modified realization is based on a current realization which is a function of a first uniform random number field. At least one further uniform random number field Ui is created and a linear combination made of the first uniform random number field and the further uniform random number field or fields Ui, together with combination coefficients ri, to derive a modified non-uniform random number field V. A uniform score transformation procedure is then performed, e.g. using an empirical cumulative distribution function, on the modified non-uniform number field V, to derive a modified uniform random number field Umod. A modified realization of the model can then be derived from the uniform random number field Umod.

Abstract: A computer-aided method of downscaling a three-dimensional geological model by generating numerical stochastic fine-scale models conditioning to data of different scales and capturing spatial uncertainties which involves a downscaling algorithm.

Abstract: A method for modeling a reservoir is described. One example of a method for modeling a 3D reservoir involves using multiple-point simulations with 2D training images.

Abstract: A multiple point simulation technique for generating a model realization of a subterranean formation having different facies is described which uses a non-stationary training image which reflects facies spatial trends across the formation. The realization is formed by sequentially populating each cell; a facies pattern of neighboring cells is identified for each cell in the model grid, and then corresponding facies patterns identified in the training image. The probability of a target cell in the model grid having a given facies is calculated based on the proportion of occurrences of the corresponding facies pattern where the central cell has that facies. The contribution of each corresponding facies pattern occurrence in the training image to this proportion or probability is weighted according to the distance between its central cell and the training image cell corresponding in location to the target cell in the model grid.

Abstract: The invention relates to a method for history matching a facies geostatistical model using the ensemble Kalman filter (EnKF) technique. The EnKF is not normally appropriate for discontinuous facies models such as multiple point simulation (MPS). In the method of the invention, an ensemble of realizations are generated and then uniform vectors on which those realizations are based are transformed to Gaussian vectors before applying the EnKF to the Gaussian vectors directly. The updated Gaussian vectors are then transformed back to uniform vectors which are used to update the realizations. The uniform vectors may be vectors on which the realizations are based directly; alternatively each realization may be based on a plurality of uniform vectors linearly combined with combination coefficients. In this case each realization is associated with a uniform vector made up from the combination coefficients, and the combination coefficient vector is then transformed to Gaussian and updated using EnKF.

Abstract: A method for creating a modified realization of a geostatistical model of a subterranean hydrocarbon reservoir is described, which may be used in a history matching process. The modified realization is based on a current realization which is a function of a first uniform random number field. At least one further uniform random number field Ui is created and a linear combination made of the first uniform random number field and the further uniform random number field or fields Ui, together with combination coefficients r to derive a modified non-uniform random number field V. A uniform score transformation procedure is then performed, e.g. using an empirical cumulative distribution function, on the modified non-uniform number field V, to derive a modified uniform random number field Umod. A modified realization of the model can then be derived from the uniform random number field Umod.

Abstract: A computer-aided method of downscaling a three-dimensional geological model by generating numerical stochastic fine-scale models conditioning to data of different scales and capturing spatial uncertainties which involves a downscaling algorithm.