1-20 of 23
Data mining
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Section-Based Data Analytics to Optimize Unconventional Development and Completions

Jesse Rossman, Karn Agarwal, Howard Melcher, Mike Mayerhofer
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4042757-MS
https://doi.org/10.15530/urtec-2024-4042757
Add To Cart
View Paper titled, Section-Based Data Analytics to Optimize Unconventional Development and Completions
Open the PDF for Section-Based Data Analytics to Optimize Unconventional Development and Completions in another window
Add to Citation Manager
Proceedings Papers

Estimated Ultimate Recovery in Horizontal Wells: A Data-Driven Approach

Ahmed Alzahabi, Ahmed Kamel, Alex Trindade
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 17–19, 2024
Paper Number: URTEC-4048995-MS
https://doi.org/10.15530/urtec-2024-4048995
Add To Cart
View Paper titled, Estimated Ultimate Recovery in Horizontal Wells: A Data-Driven Approach
Open the PDF for Estimated Ultimate Recovery in Horizontal Wells: A Data-Driven Approach in another window
Add to Citation Manager
Proceedings Papers

Data-Driven Analysis for Causality of Parent–Child Interactions in the Bakken

Kyoung Suk Min, Alexander V. Chakhmakhchev, Xue Yu, Nicholas A. Azzolina, Darren D. Schmidt, Bethany A. Kurz, James A. Sorensen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3862589-MS
https://doi.org/10.15530/urtec-2023-3862589
Add To Cart
View Paper titled, Data-Driven Analysis for Causality of Parent–Child Interactions in the Bakken
Open the PDF for Data-Driven Analysis for Causality of Parent–Child Interactions in the Bakken in another window
Add to Citation Manager
Proceedings Papers

Completion Optimization in the Bakken Petroleum System Using Data Mining

Alexander Chakhmakhchev, Nicholas A. Azzolina, Bethany A. Kurz, Xue Yu, Chantsalmaa Dalkhaa, Justin T. Kovacevich, James A. Sorensen
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723843-MS
https://doi.org/10.15530/urtec-2022-3723843
Add To Cart
View Paper titled, Completion Optimization in the Bakken Petroleum System Using Data Mining
Open the PDF for Completion Optimization in the Bakken Petroleum System Using Data Mining in another window
Add to Citation Manager
Proceedings Papers

Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs

Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib Asl
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703284-MS
https://doi.org/10.15530/urtec-2022-3703284
Add To Cart
View Paper titled, Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
Open the PDF for Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs in another window
Add to Citation Manager
Proceedings Papers

A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5059-MS
https://doi.org/10.15530/urtec-2021-5059
Add To Cart
View Paper titled, A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells
Open the PDF for A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells in another window
Add to Citation Manager
Proceedings Papers

Data to Decision: A Unified and Rapid Workflow for Unconventional Reservoirs Blending Data Analytics, Physics-Based Completion Optimization, and Investor- Oriented Economics

George Voneiff, Peter Bastian
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5454-MS
https://doi.org/10.15530/urtec-2021-5454
Add To Cart
View Paper titled, Data to Decision: A Unified and Rapid Workflow for Unconventional Reservoirs Blending Data Analytics, Physics-Based Completion Optimization, and Investor- Oriented Economics
Open the PDF for Data to Decision: A Unified and Rapid Workflow for Unconventional Reservoirs Blending Data Analytics, Physics-Based Completion Optimization, and Investor- Oriented Economics in another window
Add to Citation Manager
Proceedings Papers

Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs

Y. Kocoglu, M. E. Wigwe, G. Sheldon, C. Watson
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3108-MS
https://doi.org/10.15530/urtec-2020-3108
Add To Cart
View Paper titled, Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs
Open the PDF for Machine Learning Based Decline Curve - Spatial Method to Estimate Production Potential of Proposed Wells in Unconventional Shale Gas Reservoirs in another window
Add to Citation Manager
Proceedings Papers

Spatio-temporal Models for Big Data and Applications on Unconventional Production Evaluation

M. E. Wigwe, E. S. Bougre, M. C. Watson, A. Giussani
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2855-MS
https://doi.org/10.15530/urtec-2020-2855
Add To Cart
View Paper titled, Spatio-temporal Models for Big Data and Applications on Unconventional Production Evaluation
Open the PDF for Spatio-temporal Models for Big Data and Applications on Unconventional Production Evaluation in another window
Add to Citation Manager
Proceedings Papers

Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale

Ali Rezaei, Fahd Siddiqui, Birol Dindoruk, M. Y. Soliman
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2751-MS
https://doi.org/10.15530/urtec-2020-2751
Add To Cart
View Paper titled, Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale
Open the PDF for Utilizing a Global Sensitivity Analysis and Data Science to Identify Dominant Parameters Affecting the Production of Wells and Development of a Reduced Order Model for the Eagle Ford Shale in another window
Add to Citation Manager
Proceedings Papers

New Engineering and Geologic Parameters to Predict Infill Well Performance in the Wolfcamp of the Delaware Basin

S. Amin Gherabati, Katie M. Smye, Guin McDaid, Scott Hamlin
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3077-MS
https://doi.org/10.15530/urtec-2020-3077
Add To Cart
View Paper titled, New Engineering and Geologic Parameters to Predict Infill Well Performance in the Wolfcamp of the Delaware Basin
Open the PDF for New Engineering and Geologic Parameters to Predict Infill Well Performance in the Wolfcamp of the Delaware Basin in another window
Add to Citation Manager
Proceedings Papers

Permian Delaware Basin Wolfcamp A Formation Productivity Analysis and Technically Recoverable Resource Assessment

Yang Qian, Frank Male, Svetlana A. Ikonnikova, Katie Smye, Guin McDaid, Emery Goodman
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-3167-MS
https://doi.org/10.15530/urtec-2020-3167
Add To Cart
View Paper titled, Permian Delaware Basin Wolfcamp A Formation Productivity Analysis and Technically Recoverable Resource Assessment
Open the PDF for Permian Delaware Basin Wolfcamp A Formation Productivity Analysis and Technically Recoverable Resource Assessment in another window
Add to Citation Manager
Proceedings Papers

Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play

Luisa Porras, Christopher Hawkes, Arshad Islam
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2938-MS
https://doi.org/10.15530/urtec-2020-2938
Add To Cart
View Paper titled, Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play
Open the PDF for Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play in another window
Add to Citation Manager
Proceedings Papers

Spider Bots: Database Enhancing and Indexing Scripts to Efficiently Convert Raw Well Data Into Valuable Knowledge

Gurtej Saini, Hong-Chih Chan, Pradeepkumar Ashok, Eric van Oort, Michael Behounek, Taylor Thetford, Mojtaba Shahri
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2902181-MS
https://doi.org/10.15530/URTEC-2018-2902181
Add To Cart
View Paper titled, Spider Bots: Database Enhancing and Indexing Scripts to Efficiently Convert Raw Well Data Into Valuable Knowledge
Open the PDF for Spider Bots: Database Enhancing and Indexing Scripts to Efficiently Convert Raw Well Data Into Valuable Knowledge in another window
Add to Citation Manager
Proceedings Papers

Using Data Analytics to Maximize Value in the Denver-Julesburg Basin

Kristina Repchuk, Aaron Reimchen, Dane Gregoris
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2902938-MS
https://doi.org/10.15530/URTEC-2018-2902938
Add To Cart
View Paper titled, Using Data Analytics to Maximize Value in the Denver-Julesburg Basin
Open the PDF for Using Data Analytics to Maximize Value in the Denver-Julesburg Basin in another window
Add to Citation Manager
Proceedings Papers

Optimization of Completion and Well Spacing for Development of Multi-Stacked Reservoirs Using Integration of Data Analytics, Geomechanics and Reservoir Flow Modeling

Kyoung Suk Min, Vikram Sen, Lujun Ji, Richard B. Sullivan
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2897656-MS
https://doi.org/10.15530/URTEC-2018-2897656
Add To Cart
View Paper titled, Optimization of Completion and Well Spacing for Development of Multi-Stacked Reservoirs Using Integration of Data Analytics, Geomechanics and Reservoir Flow Modeling
Open the PDF for Optimization of Completion and Well Spacing for Development of Multi-Stacked Reservoirs Using Integration of Data Analytics, Geomechanics and Reservoir Flow Modeling in another window
Add to Citation Manager
Proceedings Papers

Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity

Daria Khvostichenko, Sergey Makarychev-Mikhailov
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2903086-MS
https://doi.org/10.15530/URTEC-2018-2903086
Add To Cart
View Paper titled, Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity
Open the PDF for Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity in another window
Add to Citation Manager
Proceedings Papers

Continental Production Allocation and Analysis Through Big Data

Beau T. Rollins, Andy Broussard, Billy Cummins, Akim Smiley, Nelson Dobbs
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2678296-MS
https://doi.org/10.15530/URTEC-2017-2678296
Add To Cart
View Paper titled, Continental Production Allocation and Analysis Through Big Data
Open the PDF for Continental Production Allocation and Analysis Through Big Data in another window
Add to Citation Manager
Proceedings Papers

The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics

Kathryn Ball, Tristan Arbus, Uchenna Odi, Jessamyn Sneed
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2668073-MS
https://doi.org/10.15530/URTEC-2017-2668073
Add To Cart
View Paper titled, The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics
Open the PDF for The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics in another window
Add to Citation Manager
Proceedings Papers

Predicting ESP Lifespan With Machine Learning

Jessamyn Sneed
Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 24–26, 2017
Paper Number: URTEC-2669988-MS
https://doi.org/10.15530/URTEC-2017-2669988
Add To Cart
View Paper titled, Predicting ESP Lifespan With Machine Learning
Open the PDF for Predicting ESP Lifespan With Machine Learning in another window
Add to Citation Manager

Product(s) added to cart

Go to Cart Continue Browsing
Close Modal