Abstract: Methods and systems for determining wellpath tortuosity are disclosed. In accordance with an embodiment, a tortuosity of a borehole segment is determined and a tortuosity of a casing associated with the borehole segment is determined based, at least in part, on the tortuosity of the borehole segment and a path conformity characteristic of the casing. A tortuosity variation factor is generated based on a value of the tortuosity of the casing relative to a value of the tortuosity of the borehole segment.

Abstract: The disclosure presents processes and methods for determining an adjusted drag friction factor, where the adjusting utilizes a hole cleaning function. In some aspects, the drag friction factor utilizes viscous drag. In some aspects, the drag friction factor utilizes viscous torque. In some aspects, the drag friction factor can be utilized to determine one or more decomposed friction factors. The decomposed friction factors or the adjusted drag friction factor can be utilized in a friction processor to improve the efficiency of borehole operations. The hole cleaning function can utilize various parameters, for example, a cuttings density, a cuttings load, a cuttings shape, a cuttings size, a deviation, a drill pipe rotation rate, a drill pipe size, a flow regime, a hole size, a mud density, a mud rheology, a mud velocity, a pipe eccentricity, and other parameters. A system is disclosed that is capable of implementing the processes and methods.

Abstract: A computer system and method for analyzing effectiveness of applied treatments or characterizing landing zones. The method includes, for respective one or more wells, receive production data, determining oil, gas, and/or water production data from the production data, determining an effective fluid production time for production of the oil and/or gas, combining the oil, gas, and/or water production data per to obtain a fluid production per time interval, calculate a fluid production rate per time interval as a function of the effective fluid production time and the fluid production of the time interval, and normalize the fluid production rate per time interval using a normalization factor that is available in the production data received. The method further includes analyzing effectiveness of treatments applied to one of the wells using the well's normalized fluid production rate associated with the plurality of time intervals.

Abstract: A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.

Abstract: The disclosure presents processes and methods for decomposing friction factors and generating a calibrated friction factor and adjusted input parameters. The calibrated friction factor and adjusted input parameters can be utilized by a borehole system as an input to adjust borehole operations to improve the operational efficiency. The friction factors can be decomposed by type, such as geometrical, geomechanical, mechanical, and fluid. The disclosure also presents processes and methods for identifying an outlier portion of a friction factor, as identified by a deviation threshold that can be used to identify adjustments to borehole operations in that portion of the borehole. A system is disclosed that is capable of implementing the processes and methods in a borehole operation system, such as a downhole system, a surface system, or a distant system, for example, a data center, cloud environment, lab, corporate office, or other location.

Abstract: The disclosure presents processes and methods for utilizing one or more micro-services to generate a calibration of a factor of a borehole operation or to generate an optimization adjustment to the borehole operation. The micro-services selected for execution can be selected by an optimization workflow, where each type of borehole operation can have its own set of micro-services. The micro-services can be part of one or more computing systems, such as a downhole system, a surface system, a well site controller, a cloud service, a data center service, an edge computing system, other computing systems, or various combinations thereof. Also disclosed is a system for implementing micro-services on one or more computing systems to enable a light weight and fast response, e.g., real-time or near real-time response, to borehole operations.

Abstract: Hybrid 3D geocellular grids are generated to represent a subset of a natural fracture network (“NFN”) directly in the simulation, while the remainder of the NFN is approximated by a multi-continuum formulation. The resulting output is a 3D geocellular grid that possesses a higher level of mesh resolution in those areas surrounding the first fracture subsets, and lower mesh resolution in the areas of the second fracture subset.

Abstract: Systems and methods of the present disclosure are directed to reservoir simulation modeling using upon rock compaction tables derived from physical pore compressibility tests. The illustrative methods transform rock mechanics-based pore compressibility tests into compliant rock compaction tables for reservoir simulators using Dimensionless Stress to Pore Pressure Conversion, to thereby transfer geomechanical changes due to confining stress into expressions of geomechanical changes due to pore pressure.

Abstract: A system includes a processing device and a non-transitory computer-readable medium having instructions stored thereon that are executable by the processing device to cause the system to perform operations. The operations include generating and running a reservoir simulation model. The reservoir and simulation model includes representative natural fracture or secondary porosity attributes for an area of interest for one or more wells. The operations also include generating a synthetic G-function response using results of the reservoir simulation model. Additionally, the operations include calibrating the synthetic G-function response from the reservoir simulation model to a field G-function response generated using results of a field diagnostic fracture injection test by changing natural fracture characteristics of the reservoir simulation model.

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: 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: Systems and methods can automatically and dynamically determine an optimum frequency for data being input into a drilling optimization tool in order to provide predictive modeling for well drilling operations. The methods and systems selectively input sets of data having different frequencies into the drilling optimization tool to build different predictive models at different frequencies. An optimization algorithm such as Bayesian optimization is then applied to the models to identify in real time an optimum frequency for the data sets being input into the drilling optimization tool based on current operational and environmental parameters.

Abstract: Fluid production can be simulated using a reservoir model and a tubing model. For example, pressure data and saturation data can be received from a reservoir model simulating a hydrocarbon reservoir in a subterranean formation. A tubing model can be generated by performing nodal analysis using the pressure data and the saturation data. A well-test result can be received that indicates an amount of fluid produced by the wellbore at a particular time. A tuned tubing model can be generated by adjusting the tubing model such that a tubing-model estimate of the amount of fluid produced by the wellbore at the particular time matches the well-test result. An estimated amount of fluid produced by the wellbore can then be determined using the tuned tubing model. The estimated amount of fluid produced by the wellbore may be used for production allocation or controlling a well tool.

Abstract: A physics-influenced deep neural network (PDNN) model, or a deep neural network incorporating a physics-based cost function, can be used to efficiently denoise sensor data. To generate the PDNN model, noisy sensor data is used as training data input to a deep neural network and training output is valuated with a cost function that incorporates a physics-based model. An autoencoder can be coupled to the PDNN model and trained with the reduced-noise sensor data which is output from the PDNN during training of the PDNN or with a separate set of sensor data. The autoencoder detects outliers based on the reconstructed reduced-noise sensor data which it generates. Denoising sensor data by leveraging an autoencoder which is influenced by the physics of the underlying domain based on the incorporation of the physics-based model in the PDNN facilitates accurate and efficient denoising of sensor data and detection of outliers.

Abstract: An abnormal trend detection system for detecting one or more hazards may provide for a safe and effective drilling operation as any of the one or more hazards may be avoided. Several indicators may be defined including a first, second, third and fourth indicator. The indicators are used to identify in a trend analysis abnormal trends. One or more thresholds may be defined. When a trend analysis indicates that a threshold has been reached or exceeded an alarm may be triggered, a drilling operation may be altered or a combination thereof.

Abstract: Systems, methods, and computer-readable media for a well construction activity graph builder. An example method can include obtaining a stream of events associated with a wellbore; obtaining mapping metadata identifying data points to be included in a graph data model from a store of data associated with the wellbore; generating the graph data model based on the stream of events, the mapping metadata, and the data points identified in the mapping metadata, the graph data model including nodes representing logical entities associated with the data points, the nodes having interconnections based on data relationships defined in the mapping metadata, each logical entity corresponding to a set of data points from the data points; and generating a view of the graph data model, the view depicting at least some of the nodes and interconnections in the graph data model.

Abstract: Systems and methods for interpreting and visualizing multi-Z horizons from seismic data are disclosed. A two-dimensional (2D) representation of seismic data is displayed via a graphical user interface (GUI). User input is received via the GUI for interpreting a multi-Z horizon within a portion of the displayed 2D representation. The user's input is tracked relative to displayed 2D representation within the GUI. Based on the tracking, each of a plurality of surfaces for the multi-Z horizon is determined. At least one intersection point between the multi-Z horizon surfaces is identified. A depth position for each surface relative to other surfaces is determined. The 2D representation of the seismic data is dynamically updated to include visual indications for the plurality of surfaces and the intersection point(s), based on the depth position of each surface, where the visual indications use different visualization styles to represent the surfaces and intersection point(s).

Abstract: A method and system for forming Representative Elemental Length (REL) of well data. The method may comprise inputting log data from a borehole in a formation into an information handling system, identifying an initial length of the REL section and divide the log data into a plurality of REL investigation sections that are of substantially equal length, calculating an average value of a formation property for each of the plurality of REL investigation sections, and selecting a maximum value and a minimum value of the REL investigation sections. The method may further comprise checking the maximum value and the minimum value are stable, deriving the formation property for each of the REL sections as an output, and forming a model of the formation from each of the REL sections. The system may comprise a downhole device, configured to take measurements of a formation, and an information handling system.

Abstract: The present disclosure is related to improvements in methods for evaluating formation fluid properties of interest in an in-production wellbore as well as evaluating health and functionalities of physical sensors present in and collecting data within the well. In one aspect, a method includes receiving data from one or more physical sensors within a wellbore; determining at least one formation property of the wellbore using one or more machine learning models receiving the data as input and generating reservoir simulation models using the at least one formation property.

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b