2D slices/images may be extracted from a three-dimensional volume of subsurface data. Image comparison analysis across sequential 2D slices/images may identify boundaries within the corresponding subsurface region, such as changes in style of deposition or reservoir property distribution. Identification of temporal/spatial boundaries in the subsurface region where subsurface properties change may facilitate greater understanding of the scales and controls on heterogeneity, and connectivity between different locations.

Screen-outs can be detected from acoustical signals in a wellbore. Data based on an acoustic signal generated during a hydraulic fracturing operation in a wellbore formed through a subterranean formation can be received. An expected total flow rate of fluid being injected into the wellbore can be determined based on the data. An actual total flow rate of the fluid being injected into the wellbore can be determined. A screen-out that occurred can be identified by comparing the expected total flow rate and the actual total flow rate.

This disclosure presents systems, methods, and apparatus for determining cluster efficiency during hydraulic fracturing, the method comprising: measuring acoustic vibrations in fracking fluid in a fracking wellhead, circulating fluid line, or standpipe of a well; converting the acoustic vibrations into an electrical signal in a time domain; recording the electrical signal to memory; analyzing the electrical signal in the time domain for a window of time and identifying two amplitude peaks corresponding to a fracture initiation; measuring a time between the two amplitude peaks; dividing the time by two to give a result; multiplying the result by a speed of sound in the fracking fluid to give a distance between the fracture initiation and a plug at an end of a current fracking stage of the well; and returning a location of the fracture initiation to an operator based on the distance between the fracture initiation and the plug.

This disclosure presents systems, methods, and apparatus for determining cluster efficiency during hydraulic fracturing, the method comprising: measuring acoustic vibrations in fracking fluid in a fracking wellhead, circulating fluid line, or standpipe of a well; converting the acoustic vibrations into an electrical signal in a time domain; recording the electrical signal to memory; analyzing the electrical signal in the time domain for a window of time and identifying two amplitude peaks corresponding to a fracture initiation; measuring a time between the two amplitude peaks; dividing the time by two to give a result; multiplying the result by a speed of sound in the fracking fluid to give a distance between the fracture initiation and a plug at an end of a current fracking stage of the well; and returning a location of the fracture initiation to an operator based on the distance between the fracture initiation and the plug.

Method and systems are provided that create one or more models of a subsurface geological formation (such as a reservoir characterization model of a hydrocarbon reservoir or a model of some other subsurface geological formation). The method and systems are configured to extend a machine learning ensemble (such as an ensemble tree-based machine learning model such as a random forest learning model) to use or embed data derived from one or more secondary models as part of the training operations of the machine learning ensemble and online use of the trained machine learning ensemble. Such data can provide information that supplements the information contained in the training data/input data.

This disclosure presents a system, method, and apparatus for classifying fracture quantity and quality of fracturing operation activities during hydraulic fracturing operations, the system comprising: a sensor coupled to a fracking wellhead, circulating fluid line, or standpipe of a well and configured to convert acoustic vibrations infracking fluid in the fracking wellhead into an electrical signal; a memory configured to store the electrical signal; a converter configured to access the electrical signal from the memory and convert the electrical signal in a window of time into a current frequency domain spectrum; a machine-learning system configured to classify the current frequency domain spectrum, the machine-learning system having been trained on previous frequency domain spectra measured during previous hydraulic fracturing operations and previously classified by the machine-learning system; and a user interface configured to return a classification of the current frequency domain spectrum to an operator of the fracking wellhead.

An illustrative geologic modeling method may comprise: obtaining a geologic model representing a subsurface region in physical space, the subsurface region being divided into multiple zones; sequentially generating a physical space simulation mesh for each of said multiple zones by: (a) mapping a current zone of the physical space geologic model to a current zone of a design space model representing a current zone of an unfaulted subsurface region; (b) gridding the design space model to obtain a design space mesh; (c) partitioning cells in the current zone of the design space mesh with faults mapped from the current zone of the physical space geologic model, thereby obtaining a partitioned design space mesh for the current zone; and (d) reverse mapping the partitioned design space mesh for the current zone to the physical space for the current zone.

2D slices/images may be extracted from a three-dimensional volume of subsurface data. Image comparison analysis across sequential 2D slices/images may identify boundaries within the corresponding subsurface region, such as changes in style of deposition or reservoir property distribution. Identification of temporal/spatial boundaries in the subsurface region where subsurface properties change may facilitate greater understanding of the scales and controls on heterogeneity, and connectivity between different locations.

System and methods of controlling fracture growth during multi-well stimulation treatments. The flow distribution of treatment fluid injected into first and second well formation entry points along multiple wellbores is monitored during a current stage of a multi-well, multistage stimulation treatment. Upon determining the fracture growth and/or 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. A portion of the fluid to be injected into the first well and/or second well 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.

Embodiments of the present invention help in the processing and interpretation of seismic survey data, by correlating or otherwise comparing or associating seismic data obtained from a seismic survey with flow information obtained from a well or borehole in the surveyed area. In particular, embodiments of the present invention allow for flow data representing a flow profile along a well that is being monitored by a distributed acoustic sensor to be determined, such that regions of higher flow in the well can be determined. For example, in the production zone the well will be perforated to allow oil to enter the well, but it has not previously been possible to determine accurately where in the production zone the oil is entering the well. However, by determining a flow rate profile along the well using the DAS then this provides information as to where in the perforated production zone oil is entering the well, and hence the location of oil bearing sands. This location can then be combined or otherwise correlated, used, or associated with petroleum reservoir location information obtained from the seismic survey, to improve the confidence and/or accuracy in the determined petroleum reservoir location.