A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.

Embodiments described herein include a system for generating a drainage radius log per well that includes a computing device that receives well data associated with a plurality of wells, utilizes the well production data to calculate a value for cumulative liquid produced by each of the plurality of wells for a predetermined time period, and utilizes at least a portion of the well data to calculate a fractional contribution for each of the plurality of wells. In some embodiments the computing device utilizes the value for cumulative liquid produced for each of the plurality of wells and the fractional contribution to calculate a cumulative liquid production for each of the plurality of wells, utilizes the cumulative liquid production to calculate the drainage radius log for each of the plurality of wells, and outputs the drainage radius log for display.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A method of estimating a depth of a hydrocarbon-water contact of a hydrocarbon reservoir in a structure. The method may include the steps of analysing one or more samples obtained from the structure to generate a relationship relating resistivity to hydrocarbon-water contact depth, obtaining a resistivity measurement of the hydrocarbon reservoir, and estimating the hydrocarbon-water contact depth from the relationship relating resistivity to hydrocarbon-water contact depth and the resistivity measurement of the hydrocarbon reservoir.

Examples described herein provide a computer-implemented method that includes analyzing a first dataset by applying the first dataset to a first model to generate a first result. The method further includes analyzing a second dataset by applying the second dataset to a second model to generate a second result. The method further includes performing validation on the first model and the second model by comparing the first result to the second result. The method further includes, responsive to determining that the first result and the second result match, modifying an operational action of a surface assembly based on at least one of the first result or the second result. The method further includes, responsive to determining that the first result and the second result do not match, updating at least one of the first model or the second model.

A system and method for uncertainty estimation of reservoir parameters in unconventional reservoirs using a physics-guided convolutional neural network to generate a plurality of reservoir models, a data analysis step, and an uncertainty step is disclosed. The method is a computationally efficient method to estimate uncertainties in models of unconventional reservoirs.

The present invention is a method of simulating fluid flows in an underground formation comprising a fracture network. A porosity model is constructed, comprising a first medium representative of an unfractured matrix, a second medium representative of fractures oriented in a first direction and a third medium representative of fractures oriented in a second direction orthogonal to the first direction. From at least the porosity model, flow parameters of a grid representation of the formation are determined, which include conduction and convection transmissibilities between two neighboring cells for the second and third media, as well as mass and energy exchanges by convection and conduction between each medium taken two by two for a single cell. Flows in the formation are simulated by f a flow simulator implementing the porosity model.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

The disclosure presents processes to improve the calibration of adjustable choke valves corresponding to a specific size of positive choke bean. Typically, manufacturers specify a position of the adjustable choke valve that corresponds to a specific choke bean size. Hydrocarbon fluid conditions and composition vary and subterranean formation characteristics vary which can lead to errors in the calibration. By comparing flow rate parameters of the hydrocarbon fluid flowing through the adjustable choke manifold and the positive choke manifold, errors in calibration can be detected and corrected. The factors involved with the hydrocarbon fluid and the error correction can be used to update a choke model. The choke model can then be used for future calibrations of the adjustable choke valve.