... erosion petroleum engineer test series 2 hole size tank perforation machine learning proppant placement hydraulic fracturing mesh sand friction reducer perforation erosion spe hydraulic fracturing technology conference Initial investigations quickly determined surface testing in a service...

... understand the effects of differing well spacing and infill timing on child well performance in different US unconventional basins. energy economics production monitoring reservoir characterization asset and portfolio management reservoir surveillance complex reservoir machine learning data...

... with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore while actively fracturing...

... The sequence of steps from a machine learning framework are outlined below. Create a preliminary geological profile, a minifrac pump schedule and a fracpack pump schedule in a fracturing design simulator. The geological profile is based on various inputs such as Pore Pressure Fracture...

... designs. production monitoring production control geochemical characterization upstream oil & gas oil sample hydraulic fracturing contribution drainage frac height communication reservoir surveillance well communication information well performance machine learning artificial...

... of digitization, Machine Learning and AI must be leveraged, and stakeholders must be at the forefront of innovation and digitally enabled operations, software and technology, and must operate with agility and with a collaborative spirit. The following are key capabilities needed to achieve "Push Button Fracturing...

... wells are incorporated in the simulation of physical scenarios characterized by single and multiple hydraulic fractures. For each specific scenario we train and test an Artificial Neural Network (ANN) with position and time as input variables, and axial displacement as output. The Machine Learning (ML...

... during fracturing. machine learning artificial intelligence hydraulic fracturing deep learning optimization problem efficiency december 2021 neural network breakdown platform control variable effectiveness real time system fuzzy logic upstream oil & gas dlm treatment data...

... Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled...

... in hydraulic fracture propagation parameters, can be incorporated in perforation cluster design. machine learning upstream oil & gas perforation diameter perf cluster artificial intelligence sdt pressure drop perforation efficiency hydraulic fracturing stimulation slurry distribution...

... data quality reservoir geomechanics machine learning artificial intelligence reservoir characterization hydraulic fracturing natural fracture g-function analysis upstream oil & gas heat exchange soliman correction fracture closure pressure dfit geothermal reservoir drillstem...

... combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore(s) while...

... on the necessary chemical package to effectively treat a well. This paper provides a holistic view of fluid selection issues and shows a real-data focused methodology to further support a leaner approach to hydraulic fracturing. machine learning energy economics fracturing materials artificial...

... machine learning pressure behavior proppant concentration hydraulic fracturing trouble stage artificial intelligence treatment plot slurry rate friction reducer petroleum engineer operation perforation upstream oil & gas signature algorithm pressure increase proppant step...

... individual well economics by adjusting completion design in these areas. chemical tracer data mining complex reservoir perforated interval machine learning artificial intelligence tracer test analysis curvature correlation tracer recovery identify performance driver reservoir...

... intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development machine learning artificial...

... which characterized the impact of stimulation parameters on resulting fracture geometry and production performance. machine learning reservoir geomechanics hydraulic fracturing modeling & simulation energy economics artificial intelligence reservoir characterization upstream oil...

... by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties. hydraulic fracturing potential outcome machine...

... for overall well productivity improvement in an unconventional reservoir. hydraulic fracturing dune proppant dune connectivity machine learning proppant slickwater proppant particle inlet boundary artificial intelligence upstream oil & gas port conductivity petroleum engineer fracture...

... at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well. machine learning complex...