Identifying a low permeable conglomerate diagenetic trap can be implemented according to a method that comprises: determining a first relation curve between a depth and a critical physical property of a known diagenetic trap in a target work area, and a second relation curve between a reservoir physical property of the known diagenetic trap and a designated seismic attribute; determining a third relation curve between the depth and the critical physical property in the target work area and the designated seismic attribute according to the first relation curve and the second relation curve; and performing a diagenetic trap identification of the target work area according to the third relation curve. Identification accuracy of a low permeable conglomerate diagenetic trap can thereby be improved.

Provided herein are systems and methods for hydraulic fracturing in earth. Systems may comprise a first well comprising sensors configured to collect sensor data at the first well. The calibration data may comprise sensor data, hydraulic fracture treatment conditions of the first well, geological data from an area containing the first well, or production date of oil or gas from the first well. The calibration data may be analyzed with the aid of one or more processors to generate an integrated 3-D model of hydraulic fracturing and fluid flow in the wellbore and reservoir of the first well. The system may further comprise a second well configured to operate according to simulation data generated from the integrated 3-D model of the first well and hydraulic fracture treatment conditions received from a user device. The user device may be configured to communicate with a server comprising the one or more processors.

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.

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).

Provided herein are systems and methods for generating reservoir models including the SOC nature of the Earth's crust. The methods employ seismic emission tomography (SET) to generate three dimensional models of a formation providing permeability without the need for traditional reservoir modeling techniques and allowing for the identification of naturally-occurring permeability pathways that provide accurate and precise locations for the efficient recovery of hydrocarbons or other fluids.

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.

An exploration method starts from cuttings associated with sampling intervals and well data for a well in a subsurface formation. The cuttings are prepared and analyzed to extract textural and chemical/mineralogical data for plural fragments in each sample that is made of the cuttings in one sampling interval. The method then includes matching lithotypes of rock defined according to the textural and chemical/mineralogical data for each fragment with segments of the well data in the corresponding sampling interval to obtain correspondences between the lithotypes and depth ranges. The correspondences between the lithotypes and the depth ranges may be used as constraints for seismic data inversion.

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.

Systems and methods for generating permeability scaling function for different features of interest are disclosed. Exemplary implementations may: obtain subsurface data sets; generate permeability scaling functions for individual features of interest; store the permeability scaling functions; and generate upscaled subsurface distributions using the permeability scaling functions.