Abstract: Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.

Abstract: A method for fracturing a formation is provided. Real-time fracturing data is acquired from a well bore during fracturing operation. The real-time fracturing data is processed using a recurrent neural network trained using historical data from analogous wells. A real-time response variable prediction is determined using the processed real-time fracturing data. Fracturing parameters for the fracturing operation are adjusted in real-time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that were adjusted based on the real-time response variable prediction.

Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.

Abstract: A system for determining completion parameters for a wellbore includes a sensor and a computing device. The sensor can be positioned at a surface of a wellbore to detect data prior to finishing a completion stage for the wellbore. The computing device can receive the data, perform a history match for simulation and production using the sensor data and historical data, generate inferred data for completion parameters using the historical data identified during the history match, predict stimulated area and production by inputting the inferred data into a neural network model, determine completion parameters for the wellbore using Bayesian optimization on the stimulated area and production from the neural network model, profit maximization, and output the completion parameters for determining completion decisions for the wellbore.

Abstract: Aspects of the present disclosure relate to receiving data associated with a subterranean reservoir to be penetrated by a wellbore and training a neural network with both the data and a physics-based first principles model. The neural network is then used to make predictions regarding the properties of the subterranean reservoir, and these predictions are in turn used to determine one or more controllable parameters for equipment associated with a wellbore. The controllable parameters can then be used to control equipment for formation, stimulation, or production relative to the wellbore.

Abstract: An illustrative formation flow simulation method includes: measuring horizontal and vertical permeability of at least one bed in a formation penetrated by a vertical borehole; representing the borehole as a linear, discretized borehole flow path; representing the formation as a plurality of horizontal layers, each layer of the plurality of horizontal layers being represented as a linear, discretized layer flow path; constructing a current state vector having values of flow parameters for the discretized borehole flow path and each of the discretized layer flow paths; constructing a solution matrix embodying a set of flow equations that relate the current state vector to a subsequent state vector, the flow equations employing the measured horizontal permeability for flow along the discretized layer flow path for each layer and employing the measured vertical permeability for cross-flow to or from each layer, wherein the solution matrix, current state vector, and subsequent state vector form a linear system of

Abstract: A method includes performing a first wellbore treatment operation of a wellbore, determining an operational attribute of the well in response to the first wellbore treatment operation, and determining a predicted response using a recurrent neural network and based on the operational attribute. The method also includes setting a controllable wellbore treatment attribute based, on the predicted response, and performing a second wellbore treatment operation of the wellbore based on the controllable well bore treatment attribute.

Abstract: 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.

Abstract: A system for multi-stage placement of material in a wellbore includes a recurrent neural network that can be configured based on data from a multi-stage, stimulated wellbore. A computing device in communication with a sensor and a pump is operable to implement the recurrent neural network, which may include a long short-term neural network model (LSTM). Surface data from the sensor at each observation time of a plurality of observation times is used by the recurrent neural network to produce a predicted value for a response variable at a future time, and the predicted value for the response variable is used to control a pump being used to place the material.

Abstract: A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of projection of optimal rate of penetration (ROP) and optimal controllable parameters such as weight-on-bit (WOB), and rotations-per-minute (RPM) for drilling operations. Optimum controllable parameters for drilling optimization can be predicted using a data generation model to produced synthesized data based on model physics, an ROP model, and stochastic optimization. The synthetic data can be combined with real-time data to extrapolate the data across the WOB and RPM space. The values for WOB an RPM can be controlled to steer a drilling tool. Examples of models used include a non-linear model, a linear model, a recurrent generative adversarial network (RGAN) model, and a deep neural network model.

Abstract: A system for real-time steering of a drill bit includes a drilling arrangement and a computing device in communication with the drilling arrangement. The system iteratively, or repeatedly, receives new data associated the wellbore. At each iteration, a model, for example an engineering model, is applied to the new data to produce an objective function defining the selected drilling parameter. The objective function is modified at each iteration to provide an updated value for the selected drilling parameter and an updated value for at least one controllable parameter. In one example, the function is modified using Bayesian optimization The system iteratively steers the drill bit to obtain the updated value for the selected drilling parameter by applying the updated value for at least one controllable parameter over the period of time that the wellbore is being formed.

Abstract: The disclosed embodiments include a method for determining fluid level drop and formation permeability during wellbore stimulation of a shut-in stage in real time. The method includes receiving an initial permeability value of a formation surrounding a wellbore. Further, the method includes performing, and if necessary repeating at a defined time interval, the following steps until a flowrate of stimulation fluid reaches zero or until a new pumping stage begins. The steps include solving for the flowrate of the stimulation fluid in the wellbore and computing hydrostatic pressure and a computed temperature in the wellbore. Further, the steps include updating a permeability calculation of the formation based on the flowrate of the stimulation fluid.

Abstract: System and methods of controlling fluid diversion during stimulation treatments are provided. Real-time measurements are obtained from a plurality of fiber-optic data sources at a well site during a stimulation treatment being performed along a portion of a wellbore within a subsurface formation. Fracture growth and stress within the subsurface formation surrounding the portion of the wellbore are determined as the stimulation treatment is performed, based on the real-time measurements and a fully-coupled diversion model. An amount of diverter for a diversion phase of the stimulation treatment to be performed along the portion of the wellbore is determined, based on the fracture growth and the stress within the subsurface formation. The diversion phase of the stimulation treatment is performed by injecting the amount of diverter into the subsurface formation via at least one injection point located along the portion of the wellbore.

Abstract: A system and method to determine closure pressure in a wellbore that can include, flowing a fracturing fluid into the wellbore during a fracturing operation of at least one stage and forming a fracture, sensing fluid pressure and a flow rate of the fracturing fluid during the fracturing operation and communicating the sensed data to a controller, plotting data points of the sensed data to a visualization device which is configured to visually present the data points to an operator as a plot, fitting a curve to the data points which represent statistically-relevant minimum pressure data at various flow rates, determining an intercept of the first curve with a zero flow rate axis of the plot, determining the closure pressure based on a pressure value of the intercept, and determining an average fracture permeability based on the closure pressure.

Abstract: Methods and system for modeling wellbore treatment operations in which the flow of treatment fluids may be diverted are provided. In one embodiment, the methods comprise: receiving, at a processing component, one or more treatment operation inputs characterizing a treatment operation for a wellbore system comprising a wellbore penetrating at least a portion of a subterranean formation and a treatment fluid comprising a diverter, wherein at least one of the one or more treatment operation inputs comprises the inlet concentration of the diverter in the treatment fluid; and using the processing component to determine a wellbore system pressure distribution and a wellbore system flow distribution based, at least in part, on the one or more treatment operation inputs and a diversion flow model, wherein the diversion flow model captures an effect of the diverter on fluid flow in the wellbore system.

Abstract: A system includes a processor(s), and a memory coupled to the processor(s) having instructions stored therein. When executed by the processor(s), the instructions cause the processor(s) to perform functions to: apply a treatment for stimulating production to at least a first well in a subterranean formation; determine a flow distribution based on at least one of a first-well measurement or a second-well measurement, the first-well measurement taken at the first well, and the second-well measurement taken at a second well; determine a length of a fracture between the first and second wells, based on the determined flow distribution; determine if the applied treatment at the first well interferes with the second well, based on the determined length of the fracture; and apply a diverting material at the first well if it is determined that the applied treatment interferes with the second well, in order to control well bashing.

Abstract: A system includes at least one processor, and a memory coupled to the at least one processor having instructions stored therein. When executed by the at least one processor, the instructions cause the at least one processor to perform functions including functions to: apply a treatment at a first well in the subterranean formation, wherein the treatment is for stimulating production; perform a step down analysis of the first well at each of a plurality of stages of the first well; determine a downhole flow distribution at each of the plurality of stages; develop a model for correlating the downhole flow distribution and the step down analysis at each of the plurality of stages; use the developed model to estimate a downhole flow distribution at a stage of a second well; and determine whether to bypass a deployment of the diverter material at the stage of the second well based on the estimated downhole flow distribution.

Abstract: System and methods of controlling fluid flow during reservoir stimulation treatments are provided. A flow distribution of treatment fluid injected into formation entry points along a wellbore path is monitored during a current stage of a multistage stimulation treatment. Upon determining that the monitored flow distribution meets a threshold, a remainder of the current stage is partitioned into a plurality of treatment cycles and at least one diversion phase for diverting the fluid to be injected away from one or more formation entry points between consecutive treatment cycles. A portion of the fluid to be injected into the formation entry points is allocated to each of the treatment cycles of the partitioned stage. The treatment cycles are performed for the remainder of the current stage using the treatment fluid allocated to each treatment cycle, wherein the flow distribution is adjusted so as not to meet the threshold.

Abstract: A hydraulic fracturing flow simulation method includes identifying a network of fractures including junctions where the fractures intersect. Each fracture accesses each associated junction via a respective opening. The method further includes determining a current network state that includes flow parameter values at discrete points arranged one-dimensionally along the fractures in the network and at discrete points arranged two-dimensionally across the junctions in the network. The method further includes constructing a set of equations for deriving a subsequent network state from the current network state while accounting for boundary layers at each opening. The method further includes repeatedly solving the set of equations to obtain a sequence of subsequent network states. The sequence embodies a time-dependent spatial distribution of at least one flow parameter. The method further includes displaying the time-dependent spatial distribution.