The present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and/or predicted drilling metrics.

Methods and systems are provided for inverted detection and positioning of a strip-like subterranean tunnel in a mountain mass, pertaining to the field combining theories of the discipline of geophysics and remote sensing technology. The method includes: using a model of thermal radiation between a mountain mass and an air layer in conjunction with DEM data to calculate solar radiation energy, and iteratively filtering out background heat flow field energy of the mountain mass; calculating mountain mass background heat propagation energy with reference to hyperspectral data; using a subterranean target inversion model to filter out each layer of background heat flow field energy of the mountain mass in an infrared remote sensing image, and acquiring an optimal elevation of the strip-like subterranean tunnel in the mountain mass and a disturbance signal distribution image constructed via strip-like subterranean tunnel heat flow field energy in each layer of the mountain mass; and using a Hough transform detection method to detect a straight line in the disturbance signal distribution image, performing fitting according to the principle of relevance of tunnel engineering design to acquire a detected location of the tunnel. In this way, inverted detection and positioning of a strip-like subterranean tunnel in a mountain environment is achieved.

A method of operating a reservoir simulator can include performing a time step of a reservoir simulation using a spatial reservoir model that represents a subterranean environment that includes a reservoir to generate simulation results for a first time where the simulation results include a front defined by at least in part by a gradient at a position between portions of the spatial reservoir model; predicting a position of the front for a subsequent time step for a corresponding second time using a trained machine model; discretizing the spatial reservoir model locally at the predicted position of the front to generate a locally discretized version of the spatial reservoir model; and performing a time step of the reservoir simulation using the locally discretized version of the spatial reservoir model to generate simulation results for the second time.

The present disclosure is directed to numerically estimating the shear modulus of Kerogen by using a combination of mineralogy from digital image analysis and sonic log analysis, when measured data on only one elastic constant (Bulk, Young's or P-wave modulus) is available. In some instances, elastic properties predicted from the digital images are compared with sonic, shear, and density logs, to estimate the shear modulus of kerogen. As a one-to-one correspondence is not expected between the core sub-samples and the rock unit sampled by the well logs, cross-property relations can be used to identify the suitability of the effective medium models and to iteratively determine the shear modulus of kerogen.

A method for formation evaluation may comprise forming one or more model parameters from one or more priori geological information and one or more downhole measurements, identifying one or more inversion controls, and performing a forward model operation using a piecewise polynomial model (PPM). The method may further comprise performing an optimization using at least the forward model operation, the one or more model parameters, and the one or more inversion controls, determining if a misfit between the one or more downhole measurements and the one or more model parameters is greater than or less than a threshold, and updating the forward model operation or the one or more priori geological information based at least in part on the misfit.

A method for identifying a collar using machine learning may include acquiring one or more measurements from one or more depth points within a wellbore including a tubular string, training a machine learning model using a training dataset to create a trained machine learning model, and identifying at least one hyperparameter using the trained machine learning model. The method may further include creating a synthetic model, wherein the synthetic model is defined by one or more pipe attributes, minimizing a mismatch between the one or more measurements and the synthetic model utilizing the at least one hyperparameter, updating the synthetic model to form an updated synthetic model, and repeating the minimizing the mismatch with the updated synthetic model until a threshold is met.

Some implementations relate to a method for parallelizing, by a geological data system, operations of a geostatistical simulation for a well data set via a plurality of processing elements (PEs). The method may include determining a reservoir area for the well data set. The method may include determining a set of turning band lines for the reservoir area. The method may include dividing the reservoir area into a plurality of tiles, each tile including a respective subset of the set of turning band lines. The method may include assigning at least one of the tiles to each of the PEs. The method may include determining, in parallel for each tile, intermediate results with respect to each respective subset of turning band lines. The method may include aggregating the intermediate results to form a final result of the geostatistical simulation.

Component weight and/or volume fractions of subterranean rock are determined. A formation model generates mineral and fluid concentration data from which elemental concentrations are calculated. Forward modeling produces a simulated energy spectrum, and simulation produces a simulated constraining log. Spectra is generated by detecting gamma radiation with a neutron logging tool, and a constraining log is generated. The spectrum and the simulated energy spectrum are compared with resultant error determined. The constraining log and simulated constraining log are compared with resultant error determined. The formation model generates further mineral and fluid concentration to calculate further elemental concentrations. Forward modeling produces further simulated energy spectrum signal and further constraining logs. The spectrum signals and further simulated spectrum signal are compared with resultant error determined. The constraining log and further simulated constraining log are compared, and resultant error is determined. The mineral and fluid concentration are selected that result in minimal error.

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Methods for coordinate-related despiking of hydrocarbon reservoir data include receiving, by a computer system, multiple datapoints of a geomechanical property of a hydrocarbon reservoir modeled by a three-dimensional (3D) grid. Each datapoint corresponds to 3D coordinates of the 3D grid. For each datapoint, the computer system aggregates the datapoint with a noise component generated using the 3D coordinates corresponding to the datapoint. The computer system determines that the aggregated datapoint is unique to the multiple datapoints. The computer system performs a transform on the datapoints for Gaussian simulation. A display device of the computer system generates a graphical representation of the geomechanical property of the hydrocarbon reservoir based on the Gaussian simulation of the transformed datapoints.