Abstract: The disclosure presents a technique to generate a fracture model using a differential stress map and model inputs. The technique simulates the fracture model using fracture fronts, initiated at perforations of a perforation stage of a hydraulic fracturing (HF) wellbore. Each fracture front is evaluated using a propagation step of a fracture model process. Using the relative differential stress states, a fracture pattern is composited to the fracture model. At each propagation step, the total energy available from the simulated HF fluid being pumped into the wellbore location is reduced by the amount necessary to generate the computed fractures. Once the remaining energy is reduced to a level where no further fractures can be created, or if a map boundary is encountered, the fracture model process terminates. The generated fracture model can be communicated to update HF job plans, wellbore placements, and other uses of the fracture model.

Abstract: A method includes receiving a seismic data volume comprising seismic information of subterranean formations and receiving a set of seismic traces of the seismic data volume. The method also includes, determining, along each seismic trace of the set of seismic traces, a set of seed points comprising minimum or maximum onsets. Further, the method includes sorting the set of seed points into a sorted set of seed points by absolute amplitude values of the set of seed points. Furthermore, the method includes generating a horizon representation of every seismic event in the seismic data volume by automatically tracking horizons throughout an entirety of the seismic data volume from the sorted set of seed points in an order of the absolute amplitude values of the sorted set of seed points. Additionally, the method includes generating a graphical user interface that includes the horizon representation for display on a display device.

Abstract: Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models.

Abstract: An estimated gas leak flow rate can be determined using a teaching set of concentration profiles, a regression model implemented by a machine-learning subsystem, and a subset of attributes measured within an environment. The teaching set of concentration profiles can include gas flow rates associated with relevant attributes. The regression model can be transformed into a gas leak flow regression model via the machine-learning subsystem using the teaching set. The subset of attributes measured within the environment can be applied to the gas leak flow regression model to determine other attributes absent from the subset of attributes and an estimated gas flow rate for the environment. A gas leak attenuation action can be performed in response to the estimated gas flow rate.

Abstract: A system can determine a temperature profile based on a prior production temperature profile and a reference start point pressure for a well. The system can virtually divide the well into a plurality of sections including uphill sections and downhill sections. The system can determine a gas-oil interface depth for each section of the plurality of sections from a water-oil ratio and a gas-oil ratio based on the temperature profile and the reference start point pressure. The system can determine an oil-water interface depth for each section of the plurality of sections from the gas-oil ratio and the water-oil ratio based on the temperature profile and the reference start point pressure.

Abstract: A system for determining exploration potential ranking for petroleum plays according to some aspects receives geological survey data of a geographical area to be ranked for a future petroleum play. The system generates predicted values based on the geological survey data, each predicted value indicating a probability that a portion of the basin includes a first characteristic. A set of polygons that represent the basin may be generated based on the predicted values. Each polygon represents a contiguous portion of the basin that has a same predicted value. A basin is score is generated by: generating a score for each polygon using the predicted value; and aggregating the score of each polygon of the set of polygons into the basin score. The basin score is displayed for use displaying for use in determining an area in which drilling a wellbore would have a greater probability of success.

Abstract: A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

Abstract: A system is described for calculating and outputting micro invisible lost time (MILT). The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. Time-stamp data that includes values of drilling parameters may be received about a drilling operation, and the values of drilling parameters may be classified into a rig state that includes rig activities. For each rig activity, an actual completion time may be determined and compared to an expected completion time for determining a deviation. At least one deviated activity, in which the deviation is greater than a threshold, may be determined. Deviations may be combined into MILT that can be output for controlling the drilling operation.

Abstract: A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.

Abstract: A method for creating a seamless scalable geological model may comprise identifying one or more geological scales, establishing a geological tied system, identifying one or more graphical resolution levels for each of the one or more geological scales, constructing the seamless scalable geological model, and producing a post-process model. A system for creating a seamless scalable geological model may comprise an information handling system, which may comprise a random access memory, a graphics module, a main memory, a secondary memory, and one or more processors configured to run a seamless scalable geological model software.

Abstract: A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.

Abstract: Methods and systems for solving inverse problems arising in systems described by a physics-based forward propagation model use a Bayesian approach to model the uncertainty in the realization of model parameters. A Generative Adversarial Network (“GAN”) architecture along with heuristics and statistical learning is used. This results in a more reliable point estimate of the desired model parameters. In some embodiments, the disclosed methodology may be applied to automatic inversion of physics-based modeling of pipelines.

Abstract: System and methods for simulating fluid flow during downhole operations are provided. Measurements of an operating variable at one or more locations within a formation are obtained from a downhole tool disposed in a wellbore within the formation during a current stage of a downhole operation being performed along the wellbore. The obtained measurements are applied as inputs to a hybrid model of the formation. The hybrid model includes physics-based and machine learning models that are coupled together within a simulation grid. Fluid flow within the formation is simulated, based on the inputs applied to the hybrid model. A response of the operating variable is estimated for a subsequent stage of the downhole operation along the wellbore, based on the simulation. Flow control parameters for the subsequent stage are determined based on the estimated response. The subsequent stage of the operation is performed according to the determined flow control parameters.

Abstract: Aspects and features of a system for real-time drilling using automated data quality control can include a computing device, a drilling tool, sensors, and a message bus. The message bus can receive current data from a wellbore. The computing device can generate and use a feature-extraction model to provide revised data values that include those for missing data, statistical outliers, or both. The model can be used to produce controllable drilling parameters using highly accurate data to provide optimal control of the drilling tool. The real-time message bus can be used to apply the controllable drilling parameters to the drilling tool.

Abstract: A system and method can be used for to calibrating time-lapse seismic volumes by cross-migration rescaling and reorientation for use in determining optimal wellbore placement or production in a subsurface environment. Certain aspects include methods for cross-migration of data sets processed using different migration techniques. Pre-processing of the data sets, optimization of rescaling and reorientation, and identification of adjustment parameters associated with minimum global error can be used to achieve a time-dependent formation data set that addresses error in all input data sets.

Abstract: A system includes equipment for at least one of formation of, stimulation of, or production from a wellbore, a processor, and a non-transitory memory device. The processor is communicatively coupled to the equipment. The non-transitory memory device contains instructions executable by the processor to cause the processor to perform operations comprising training a hybrid deep generative physics neural network (HDGPNN), iteratively computing a plurality of projected values for wellbore variables using the HDGPNN, comparing the projected values to measured values, adjusting the projected values using the HDGPNN until the projected values match the measured values within a convergence criteria to produce an output value for at least one controllable parameter, and controlling the equipment by applying the output value for the at least one controllable parameter.

Abstract: Geomechanical parameters can be used to optimize a well configuration that includes one or more projected wells having locations and geometries. Formation data and regional stress information of a formation can be used to determine a local stress variation of the formation. A quality index can be generated by combining petrophysical properties with the local stress variation. Hydrocarbon recovery flow simulations can be generated by generating well configuration models based on the quality index, generating reservoir geomechanical model that includes hydraulic fracture propagation characteristics, determining new hydraulic fractures by simulating propagation through the reservoir geomechanical model and using geomechanical rules, and determining a projected hydrocarbon recovery rate by simulating flow with the new hydraulic fractures. A well placement plan can be selected using the projected hydrocarbon recovery rates. The well placement plan can be output to be used to plan one or more wellbores.

Abstract: A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.

Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Abstract: A system can provide for determining characteristics loss in a wellbore. The system can include a processor and a non-transitory memory with instructions that are executable by the processor for causing the processor to execute operations. The operations can include receiving, from sensors in a wellbore, data corresponding to loss indicators. The operations can include determining a loss probability for each loss indicator. The operations can include determining a total loss probability of fluid loss in the wellbore based on the loss probabilities. The operations can include outputting the total loss probability to be used in a drilling operation in the wellbore.