A method of calculating the temperature and/or porosity of a geological structure, wherein there is provided at least two geophysical parameters of the geological structure, the method including inverting the at least two geophysical parameters to estimate the temperature and/or porosity of the geological structure.

Exemplary implementations may: obtain, from the electronic storage, geological data corresponding to the geographic volume of interest; generate a framework for sediment deposition using a first set of multiple physical, chemical, biological, and geological processes; generate a framework for diagenesis using a second set of multiple physical, chemical, biological, and geological processes; generate a representation of sediment deposition by applying the geological data corresponding to the geographic volume of interest to the framework for sediment deposition; generate a representation of diagenesis based on the framework for diagenesis and the representation of sediment deposition; and display the representation of sediment deposition and the representation of diagenesis on a graphical user interface.

Method and systems are provided that create one or more models of a subsurface geological formation (such as a reservoir characterization model of a hydrocarbon reservoir or a model of some other subsurface geological formation). The method and systems are configured to extend a machine learning ensemble (such as an ensemble tree-based machine learning model such as a random forest learning model) to use or embed data derived from one or more secondary models as part of the training operations of the machine learning ensemble and online use of the trained machine learning ensemble. Such data can provide information that supplements the information contained in the training data/input data.

Computer-implemented methods, media, and systems for characterization and geomodeling of three-dimensional (3D) vugular pore system (VPS) in carbonate reservoir are disclosed. One example method includes determining an occurrence of a VPS in multiple layers of a carbonate reservoir based on data collected from multiple wells in the carbonate reservoir. A spatial distribution of multiple VPS intensity classes of the VPS is determined using at least one of well log data, borehole image log data, production log data, or seismic acoustic impedance data from the multiple wells. A 3D VPS intensity distribution model of the VPS is constructed using the spatial distribution of the multiple VPS intensity classes of the VPS. The 3D VPS intensity distribution model is provided for at least one of reservoir volumetric estimation, reservoir history matching, or reservoir quality prediction of the carbonate reservoir.

A combining mechanism for borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is provided, said mechanism comprising: at least one mechanism for assigning interpretive values to individual processed data types; at least one mechanism for combining the interpretive value data sets; and, at least one mechanism for providing an interpretation. A method of combining borehole logging tool data that employs modality merging to combine the output data of various borehole logging tools to provide a combined result and automated interpretation is also provided, said method comprising: assigning interpretive values to individual processed data types; combining the interpretive value data sets; and, providing an interpretation.

A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).

The present invention relates to a method of prediction of hydrocarbon accumulation in a geological region comprising the following steps of: a. Generation of a geological basin model; b. Generation of a geomechanical model; c. Generation of an integrated model; d. Generation of a strain map based on the information obtained in steps a to c; e. Prediction of hydrocarbon accumulation from the strain maps.

A method may include obtaining first nuclear magnetic resonance (NMR) data and acquired permeability data regarding a geological region of interest. The method may further include determining, using a neural network and second NMR data, predicted permeability data regarding a predetermined formation within the geological region of interest. The neural network may be trained using the first NMR data and the acquired permeability data. The method may further include determining a predetermined fracture size within the predetermined formation based on the predicted permeability data. The method may further include determining a predetermined type of lost circulation material (LCM) based on the predetermined fracture size. The method may further include transmitting a command to a well system that triggers a well operation using the predetermined type of LCM.

A method, system and device for predicting an oil accumulation depth limit of deep and ultra-deep marine carbonate reservoirs is provided. The method includes: obtaining geological factors acting on a porosity of a deep/ultra-deep marine carbonate reservoir, standardizing absolute values of the geological factors; calculating a porosity of the deep/ultra-deep marine carbonate reservoir; acquiring ratios of oil, water and dry layers in each M % porosity interval, and acquiring a relationship between a dry layer ratio and the porosity of the deep/ultra-deep marine carbonate reservoir; recursively obtaining a relationship between the dry layer ratio and the burial depth and determining the oil accumulation depth limit of the deep/ultra-deep marine carbonate reservoir. The method, system and device solves the problem that the prior art cannot by predicting the oil accumulation depth limit directly through the relationship between the dry layer ratio and the depth.

Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters using a lithology porosity machine learning model without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. The lithology porosity machine-learning model may be trained and calibrated using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.