Summary
We collected more than 500 ft of through-fracture core in the Upper Wolfcamp (UWC) and Middle Wolfcamp (MWC) formations in the Permian Basin. As part of core characterization, we analyzed the core-sludge samples for the presence of proppant and natural-calcite particles. Apart from sample preparation and imaging, we designed and developed a novel image-processing work flow to detect and classify the particles. We used the observations from the identified particle distribution within the stimulated rock volume to understand proppant-transport behavior. We used relative distributions of smaller 100-mesh- and larger 40/70-mesh-proppant particles to interpret proppant placement in relation to perforation clusters. Finally, we used the relative distribution of particles to understand the interaction between natural and hydraulic fractures. We observe that stress variations and the degree of natural fracturing have a bearing on local proppant-screenout behavior. Smaller 100-mesh proppant seems to dominate at larger lateral offsets from the hydraulically fractured wells. We also observe indications of heel-side bias according to lateral proppant distribution. We share our work flow for particle detection and classification, which can serve as a template for proppant analysis in the future if significant through-fracture cores are collected in similar field experiments.
Microseismic monitoring is an increasingly common geophysical tool to monitor the changes in the subsurface. Autopicking involving phase arrival detection is a common element in microseismic data processing schemes and is necessary for accurate estimation of event locations as well as other workflows such as tomographic or moment tensor inversion, etc. The quality of first arrival picking is dependent on the actual seismic waveform, which in turn is related to the near surface and subsurface structure, source type, noise conditions, environmental factors, and monitoring array design, etc. We have developed a new hybrid autopicking workflow which makes use of multiple derived attributes from the seismic data and combines them within an artificial neural network framework. An evolutionary algorithm scheme is used as the network training algorithm. The autopicker has been tested and its applicability has been validated using a synthetically modelled seismic source, with promising results. In this work, we share the basic workflow and different attributes that have been tested with this algorithm for a synthetic data set to provide a framework for independent implementation, use and validation. We also compare the results obtained using the new neural network based autopicking routine with very robust contemporary autopicking algorithms in use within the industry.
Typical hydraulic fracturing designs in shale utilize a predetermined fluid pump rate, which once achieved is held constant throughout the treatment, excluding situations when surface pressure limitations or other conditions disallow. We propose a method of pumping hydraulic fracture stages where the fluid pump rate is rapidly changed from the predetermined maximum rate, to some significantly lower rate, and then rapidly increased back to original maximum rate. This rapid change in the flow rate produces a pressure pulse that travels up and down the wellbore and has the capacity, together with the pump rate change, to open previously unopened perforations, while increasing fracture complexity through fluid diversion.We observed increased microseismicity during hydraulic fracturing in stages with frequent pump rate changes. Regardless of their type and nature, seismic signals are indicative of fragmentation of the treated zone. This could be from shear shattering or dilatational opening. One can also assume that high signal density is a good measure of fracturing efficiency. To further investigate these observations, we implemented a variable pump rate fracture design in a Marcellus shale well. More specifically, we implemented the variable pump rate frac design in every odd stage, while implementing a constant rate design in every even stage. This was done in order to account for changes in the reservoir along the horizontal lateral.Production log results showed on average a 19% increase in production for the variable pump rate stages versus the constant pump rate stages. A lower treating pressure was often encountered after the rapid rate changes, leading to the conclusion that unopened perforations were opened with the aid of the induced pressure pulses. Total well production decline was much slower for test well that included variable pump rate changes versus the offset horizontal well which did not include the variable pump rate frac design.And finally water hammer frequency decay analysis shows a predictable trend in well with variable pump rate stages. Throughout the variable pump rate stages, no proppant transport issues were encountered and the frac stages were completed without any major issues.Rapid rate changes applied throughout the fracture treatment enhance microseismicity, which could be interpreted as additional fracture complexity. Surface fracturing pressure data shows that rapid pump rate changes open additional perforations without physical flow diverters such as ball sealers or frac balls,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.