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.

Multiphase flow meters and related methods are disclosed herein. An example apparatus includes an inlet manifold; an outlet manifold, first and second flow paths coupled between the inlet and outlet manifolds; and an analyzer to determine a flow rate of fluid flowing through the first and second flow paths based on a parameter of the fluid flowing through the first flow path.

To reduce effects of artificial alteration of measured formation pressure downhole, an iterative procedure for accurately measuring formation pressure in drawdown/buildup operations is presently disclosed. During buildup/drawdown operations, pressure measurements are taken by pressure sensors in concentric volumes sealed to the formation. After each buildup operation, pressure in the outer concentric volume is lowered using a pressure sensor therein to a progressively lower pressure until a pattern for the pressure trend stabilizes asymptotically. The true formation pressure is taken after a final buildup operation once pressure measurements stabilize.

The present disclosure relates to a method and system for predicting the production of a fractured horizontal well in a shale gas reservoir, and relates to the technical field of fractured horizontal wells. Considering the different diffusion modes of the matrix in different zones, the present disclosure uses Fick's First Law to describe the pseudo-steady-state diffusion of the matrix in the fracture network zone, Fick's Second Law to describe the unsteady-state diffusion of the matrix in the pure matrix zone, and Darcy's Law to describe the seepage in the fracture network. The present disclosure predicts the production of the fractured horizontal well in the shale gas reservoir under the conditions of matrix-microfracture coupling and hydraulically created fracture-microfracture coupling. The present disclosure improves the prediction accuracy of shale gas well production, and more accurately describes the actual flow law of the shale gas reservoir.

Examples of techniques for performing a wellbore construction operation in a wellbore are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes: performing a rheology test on a test fluid, the test fluid being representative of a fluid pumped through the wellbore, to generate test fluid data; performing, by the processing device, a data analysis on the test fluid data to generate at least one parameterized correlation; measuring a first property of the fluid to generate measured data; calculating, by the processing device, a second property of the fluid in the wellbore by using the parameterized correlations and the measured data to generate calculated data; and changing a parameter of the wellbore construction operation based at least in part on the calculated data.

The present invention discloses a three-dimensional in-situ characterization method for heterogeneity in generating and reserving performances of shale. The method includes the following steps: establishing a logging in-situ interpretation model of generating and reserving parameters based on lithofacies-lithofacies-well coupling, and completing single-well interpretation; establishing a 3D seismic in-situ interpretation model of generating and reserving parameters by using well-seismic coupling; establishing a spatial in-situ framework of a layer group based on lithofacies-well-seismic coupling, and establishing a spatial distribution trend framework of small layers of a shale formation by using 3D visualized comparison of a vertical well; and implementing 3D in-situ accurate characterization of shale generating and reserving performance parameters by using lithofacies-well-seismic coupling based on the establishment of the seismic-lithofacies dual-control parameter field. The present invention integrates an in-situ technology into shale logging, seismic generating and reserving parameter interpretation, and the establishment of a 3D mesh model of small layers of shale, which realizes the accurate description of the heterogeneity in TOC content and porosity value of shale oil and gas in a 3D space, and provides a reliable technical support for shale oil and gas exploration and development.

The shear head device includes a monitoring head having geophones and transmitters inside a cylindrical body. A shear head is coupled to the monitoring head from below. The shear head has a tubular structure with a plurality of apertures formed around an outer surface of the tubular structure. A plurality of cones are coupled with modified tips and disposed within the plurality of apertures. A sheet supports the plurality of cones inside the shear head. The sheet is selectively movable between a first radial position and a second radial position for the modified tips to apply radial force to the rock by adjustment of an internal pressure of the shear head. The transmitters transmit the recorded acoustic emission to a computing system for determining properties of the rock while the shear head device is testing the rock in the bore.

A system and method for forming a denoised seismic image of a subterranean region of interest is provided. The method includes obtaining an observed seismic dataset for a subterranean region of interest and forming a plurality of common midpoint gathers having a plurality of traces, each trace having an ordinate series of sample values, a common-midpoint location and a unique value of a secondary sorting parameter. The method further includes, for each of the plurality of common midpoint gathers, selecting a set of spatially adjacent common midpoint gathers using a spatial windowing operator and determining a weighted midpoint gather based on the common midpoint gather and the set of spatially adjacent common midpoint gathers. The method still further includes forming a denoised seismic dataset by combining the weighted midpoint gathers using an inverse spatial windowing operator and forming the denoised seismic image based on the denoised seismic dataset.

A method is disclosed for generating a machine learning model to predict a reservoir fluid property, such as gas-oil ratio or density, based on standard mud-gas and petrophysical data. It has been found that this model predicts these reservoir fluid properties with an accuracy that is close to that which can be achieved using advanced mud-gas data. This is advantageous, as than standard mud-gas data and petrophysical data is much more readily available than advanced mud-gas data.

Systems and methods for evaluating hydrocarbon properties. At least one of the systems includes: a drilling machine configured to drill a borehole; a plurality of infrared cameras configured to capture infrared image data representing a plurality of infrared images of at least one core sample extracted from the borehole; a computer-readable memory comprising computer-executable instructions; and at least one processor configured to execute the computer-executable instructions, in which when the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving the infrared image data captured by the plurality of infrared cameras; determining, based on the infrared image data, at least one hydrocarbon weight value of the at least one core sample.