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.

The present invention belongs to the technical field of petroleum exploitation engineering, and discloses a 3D modeling method for cementing hydrate sediment based on a CT image. Indoor remolding rock cores or in situ site rock cores without hydrate can be scanned by CT; a sediment matrix image stack and a pore image stack are obtained by gray threshold segmentation; then, a series of cementing hydrate image stacks with different saturations are constructed through image morphological processing of the sediment matrix image stack such as dilation, erosion and image subtraction operation; and a series of digital rock core image stacks of the cementing hydrate sediment with different saturations are formed through image subtraction operation and splicing operation to provide a relatively real 3D model for the numerical simulation work of the basic physical properties of a reservoir of natural gas hydrate.

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.

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.

Methods for determining reservoir characteristics of a well can include receiving a first core from the well; performing an experiment to determine the wave velocity associated with a first direction of the first core, the experiment including: transmitting an ultrasonic wave through the first core in the first direction; receiving the transmitted ultrasonic wave; and determining a directional wave velocity of the first core based on the transmitted ultrasonic wave and the received transmitted ultrasonic wave, wherein the directional wave velocity represents a wave velocity along the first direction; rotating the first core about a longitudinal axis of the first core; and performing the experiment along a second direction of the first core.

Methods for determining reservoir characteristics of a well can include receiving a first core from the well; performing an experiment to determine the wave velocity associated with a first direction of the first core, the experiment including: transmitting an ultrasonic wave through the first core in the first direction; receiving the transmitted ultrasonic wave; and determining a directional wave velocity of the first core based on the transmitted ultrasonic wave and the received transmitted ultrasonic wave, wherein the directional wave velocity represents a wave velocity along the first direction; rotating the first core about a longitudinal axis of the first core; and performing the experiment along a second direction of the first core.

A method, computer program product, and computing system are provided for receiving reservoir data associated with the reservoir. A simulation may be performed on the reservoir data to generate simulated reservoir data. A subset of realizations including a minimal number of realizations from a plurality of realizations may be determined based upon, at least in part, one or more statistical moments of the simulated reservoir data. An optimized reservoir model associated with an objective may be generated based upon, at least in part, the subset of realizations including the minimal number of realizations.

A method of identifying events includes obtaining an acoustic signal from a sensor, determining one or more frequency domain features from the acoustic signal, providing the one or more frequency domain features as inputs to a plurality of event detection models, and determining the presence of one or more events using the plurality of event detection models. The one or more frequency domain features are obtained across a frequency range of the acoustic signal, and at least two of the plurality of event detection models are different.