Described are systems and methods for determining a distance between a hydraulic fracture of a child well to a parent wellbore of a parent well. A distributed accoustic sensor fiber optic cable measures strain measurements along a length of the parent wellbore. A computing device then calculates the distance between the hydraulic fracture and the parent wellbore by inputting the strain measurements into a fracture strain model to solve for a location of the hydraulic fracture. A sub-surface process may be adjusted based on the calculated distance between the hydraulic fracture and the parent wellbore to avoid or utilize a frac hit.

This disclosure presents a fault prediction system using a deep learning neural network, such as a convolutional neural network. The fault prediction system utilizes as input seismic data, and then derives various seismic attributes from the seismic data. In various aspects, the seismic attributes can be normalized and have importance coefficients determined. A sub-set of seismic attributes can be selected to reduce computing resources and processing time. The deep learning neural network can utilize the seismic data and seismic attributes to determine parameterized results representing fault probabilities. The fault prediction system can utilize the fault probabilities to determine fault predictions which can be represented as a predicted new seismic data, such as using a three-dimensional image.

A fracturing treatment optimization system using multi-point pressure sensitive fiber optic cables to measure interwell fluid interaction data, microdeformation strain data, microseismic data, distributed temperature data, distributed acoustic data, and distributed strain data from multiple locations along a wellbore. The fracturing treatment optimization system may analyze the interwell fluid interaction data, microdeformation strain data, microseismic data, distributed temperature data, distributed acoustic data, and distributed strain data, modify a subsurface fracture network model, and calculate interwell fluid interaction effects. The fracturing treatment optimization system may use the fracture network model to measure current and predict future fracture growth, hydraulic pressure, poroelastic pressure, strain, stress, and related completion effects. The fracturing treatment optimization system may enable real-time monitoring and analysis of treatment and monitoring wells. The fracturing treatment optimization system may suggest and effect modifications to optimize treatment of the treatment and monitoring wells.

A data-driven linear filtering method to recover microseismic signals from noisy data/observations based on statistics of background noise and observation, which are directly extracted from recorded data without prior statistical knowledge of the microseismic source signal. The method does not depend on any specific underlying noise statistics and works for any type of noise, e.g., uncorrelated (random/white Gaussian), temporally correlated and spatially correlated noises. The method is suitable for microquake data sets that are recorded in contrastive noise environments. The method is demonstrated with both field and synthetic data sets and shows a robust performance.

An illustrative hydraulic fracture mapping method includes: collecting microseismic signals during a multistage hydraulic fracturing operation; deriving microseismic event locations from the microseismic signals to create a microseismic event map for each stage of the operation; fitting a set of fracture planes to the microseismic event maps; determining a stimulated reservoir volume (“SRV”) region for each said stage; identifying where SRV regions overlap to form an overlap region; partitioning the overlap region to eliminate any overlap between the SRV regions; truncating the set of fracture planes for the SRV regions to discard any portion outside the revised SRV regions; and storing or displaying the truncated set of fracture planes for the first revised SRV region.

A method for fracking a well to reorient vertical fractures into horizontal fractures throughout a target reservoir, including target reservoirs having multiple thin heterogeneous target production zones. The method involves sequentially fracking and injecting proppant at periodic vertical intervals, while monitoring in real time, at least until a dip of an adjacent hydraulic fracture has rotated to a horizontal orientation.

This disclosure presents a fault prediction system using a deep learning neural network, such as a convolutional neural network. The fault prediction system utilizes as input seismic data, and then derives various seismic attributes from the seismic data. In various aspects, the seismic attributes can be normalized and have importance coefficients determined. A sub-set of seismic attributes can be selected to reduce computing resources and processing time. The deep learning neural network can utilize the seismic data and seismic attributes to determine parameterized results representing fault probabilities. The fault prediction system can utilize the fault probabilities to determine fault predictions which can be represented as a predicted new seismic data, such as using a three-dimensional image.

A rock sample is nano-indented from a surface of the rock sample to a specified depth less than a thickness of the rock sample. While nano-indenting, multiple depths from the surface to the specified depth and multiple loads applied to the sample are measured. From the multiple loads and the multiple depths, a change in load over a specified depth is determined, using which an energy associated with nano-indenting rock sample is determined. From a Scanning Electron Microscope (SEM) image of the nano-indented rock sample, an indentation volume is determined responsive to nano-indenting, and, using the volume, an energy density is determined. It is determined that the energy density associated with the rock sample is substantially equal to energy density of a portion of a subterranean zone in a hydrocarbon reservoir. In response, the physical properties of the rock sample are assigned to the portion of the subterranean zone.

Systems and methods of performing a seismic survey are described. The system can receive seismic data in a first domain, and transform the seismic data into a tau-p domain. The system can identify a value on an envelope in the tau-p domain, select several values on the tau-p envelope using a threshold, and then generate a masking function. The system can combine the masking function with the tau-p transformed seismic data, and then perform an inverse tau-p transform on the combined seismic data. The system can adjust amplitudes in the inverse tau-p transformed combined seismic data, and identify one or more coherent events corresponding to subsea lithologic formations or hydrocarbon deposits.

An apparatus includes a mechanical wave source; a set of mechanical wave sensors in a borehole to provide subsurface wave measurements based on formation waves from the mechanical wave source, and a processor. The apparatus also includes a machine-readable medium having program code to acquire the subsurface wave measurements, select a first set of tool wave measurements based on the subsurface wave measurements, and generate a set of filtered subsurface wave measurements by filtering the subsurface wave measurements based on the first set of tool wave measurements. The program code also includes instructions to generate a time-domain semblance map based on the set of filtered subsurface wave measurements, wherein the time-domain semblance map includes an initial set of compression wave peaks, determine a selected qualified compression wave peak based on a semblance value in the time-domain semblance map, and determine a compression wave slowness based on the selected qualified compression wave peak.