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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3861644-MS
... to mitigate it. The problem objective is to predict when a child well frac operation will interact and affect a neighbor parent well using data-driven Machine Learning (ML) algorithms. This solution requires extensive subject matter expert (SME) validated data to create a ML training data set that can...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3860898-MS
... shut-in, leading to extended downtime and production loss. Unfortunately, it is hard to know a priori which wells would liquid load when shut in. In this work, we provide mechanistic insights as well as develop a machine learning model to quantify the risk of liquid loading in naturally flowing wells...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864794-MS
... exPlanations (SHAP), which explains the decisions the machine-learning model is making. This study develops an understanding of how liquids SHAP values are derived and how they are used to determine key geologic productivity drivers. The analysis dives into a geology SHAP map of the Wolfcamp A Upper...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862589-MS
... drillstem/well testing upstream oil & gas geologist drillstem testing complex reservoir artificial intelligence data mining economic geology completion united states government health & medicine geological subdiscipline interaction petroleum geology machine learning bakken...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3866049-MS
... is excellent, and the mean absolute percentage error for predicting the true source flux rate values of all the training images is 4.31%, for all validation images is 12.4%, and for all test images is 8.27%. Once validated against field data, a machine learning model such as this can be used as a screening...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3861300-MS
... gas machine learning fracture fracture conductivity driving bottom-line performance united states government rock type artificial intelligence operator williams-kovac subsurface dynamic inc urtec complex reservoir hydraulic fracturing mudstone mudrock workflow application fba...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864002-MS
... surveillance machine learning scenario deliverability figure 12 frequency URTeC: 3864002 Improving Artificial Lift Timing, Selection, and Operations Strategy Using a Physics Informed Data-driven Method Hardikkumar Zalavadia1, Prithvi Singh1, Utkarsh Sinha1, Sathish Sankaran1, 1. Xecta Digital Labs...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862949-MS
... understand effective hydraulic fracture geometries is still required. asia government united states government history matching artificial intelligence hydraulic fracturing well-2 well-1 conference iteration complex reservoir machine learning saturation upstream oil & gas reservoir...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3865879-MS
... drilling agent rl algorithm simulation engine algorithm machine learning optimization algorithm international drilling conference proximal policy optimization simulator URTeC: 3865879 Maximizing Efficiency of Deep-Reinforcement Learning Agents in Autonomous Directional Drilling...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864951-MS
..., influences from geology, previous fluid recovery, and inter-well spacing are less known. In this study, we use a series of machine learning models to disentangle the impacts of several well parameters and get an understanding of the conditions in which refracs produce more hydrocarbons. We first separated...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862247-MS
... facies probabilities. The CNN results better resolve the zone of interest, show better lateral continuity and better match with the well control, including two blind wells, than estimates based on a more traditional machine learning workflow using multilinear regression and hand selected attributes...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3865091-MS
... Abstract This paper introduces a novel machine learning-driven approach, using the QLog software, to estimate pore pressure at-the-bit, in real time, as a well is being drilled. The required input data is typically available on all modern drilling rigs. Initial results suggest...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862919-MS
...) and siliciclastic channel, levee, and fan lobe (fine grained sandstones) (Montgomery, 1997). geologist sedimentary rock delaware basin carbonate rock clastic rock united states government artificial intelligence reservoir simulation upstream oil & gas sand drilling operation machine learning...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3867323-MS
... machine learning far-field diverter efficiency stimulation enhancing near-field connectivity technology conference information nearfield connectivity diverter nfci optimizing completion coefficient Copyright 2023, Unconventional Resources Technology Conference ...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862321-MS
..., we use a non-linear and multivariate machine learning approach to provide descriptive evidence of the effects of existing well production on infill wells and segregate that impact into the contributions of individual features. We find that the percentage of total reserves produced by existing wells...

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