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Keywords: prediction
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702980-MS
... Abstract This study demonstrates that machine learning models trained on manually performed petrophysical analyses (n = 1542) can generate predictions with accuracy that is sufficient to make business decisions. We evaluated multiple machine learning algorithms to establish a benchmark...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3707202-MS
... learning and deep neural network to estimate and predict the geomechanical properties of the Permian Basin. The log-derived prediction algorithm includes (a) Single-Well prediction, 75% of log data of a single well is used as a specimen for training the Bi-LSTM, and the rest 25% of data of the same well...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703282-MS
... not be so straightforward and additional information about the pumping system may be needed to estimate the BHP. The goal of this work is to build a Machine Learning data-driven model that can predict the BHP for multi-fractured horizontal wells of the Vaca Muerta Formation in Argentina. Input variables...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702606-MS
... extrapolation ability and require sufficient training data, where training an under-determined neural network predictive model with limited data can result in overfitting and poor prediction performance. Unlike statistical models, physics-based models impose causal relations that can provide reliable...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703738-MS
... and fractures network scale. This work presents an empirical correlation for water-hydrocarbon relative permeability prediction in fractured reservoirs, as a function of stress state and capillary number variations usually relevant in the stimulated reservoir volume – SRV after hydraulic fracturing...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3703284-MS
..., a detailed comparative analysis of the performance of ML algorithms and statistical methods is presented to predict the oil production profiles of four hydraulically fractured horizontal wells. To construct the production time-series database, four numerical flow simulations of a tight oil Stimulated...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722179-MS
..., which proves to be efficient when dealing with time series analysis. The main target of the Seq2Seq LSTM-based model is to predict future oil rates by following a sequence series of historical data. For instance, the model uses repetitively prior timesteps to predict the next step by sliding windows...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721170-MS
..., especially in unconventional reservoirs and carbon sequestration. This special importance comes from the need for optimal predictions of natural fracture distribution for horizontal well drilling and hydraulic fracture planning, which, in turn, is of a special importance in leveraging hydrocarbon production...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3720351-MS
... coefficients from vertical seismic profiling (VSP) data or advanced dipole sonic logs. Since most of the logs don't include these data, we need a reliable method to predict the anisotropy parameters from the conventional rock property logs. For this reason, we develop a statistical approach to estimate...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3721712-MS
.... Machine learning was utilized in the first part of this study to create a model that can be used to predict the presence of natural fractures along wells. The model inputs are well logs, and the model output is the natural fracture presence or absence as derived from core. For the Upper Wolfcamp...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719366-MS
... predictions remains challenging due to the complex nature of the physical processes involved. Attempts to develop predictive models to estimate the parent-child degradation effect have included numerical simulations and various machine learning techniques. Used individually, however, these approaches...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3719984-MS
.... A machine learning (ML) model was developed with R 2 =0.80 to predict 2-year cumulative oil production (CUMO) in response to multiple input parameters, including completion design variables, geologic properties, a depletion factor for production degradation, well sequencing information (e.g., standalone...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723045-MS
... Abstract Online analysis and prediction of torque and drag (T&D) during drilling provides critical information for down-hole condition monitoring and drilling parameters optimization. It's difficult for traditional soft or stiff string methods to incorporate amounts of friction test data...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
... formations. But with the advent of current technological advancement leading to improved drilling up to and below the Wolfcamp more data is now available. Based on the needs mentioned above, the objective of this research is to develop a methodology for predicting the reservoir pressure gradient trend...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723718-MS
... with third party Novi Labs to address this challenge by using machine learning to predict new well performance and deepen insight into production drivers in the Vaca Muerta. The technical team employed a collaborative and iterative approach with third party Novi Labs to successfully build and validate...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723680-MS
... Abstract Various types of predictive models have been applied over the years to make quantitative decisions for unconventional development plans. These models are either very simple (e.g., type-curves) which ignore the reservoir physics or are too complex (e.g numerical simulation models...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723767-MS
..., as they become available. This hybrid modeling approach combines the predictive traits of physics-based modeling with the self-adjusting capabilities of machine learning, always ensuring best performance. Observations: We have demonstrated that we can deliver reliable flow rates for individual wells based...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3723603-MS
... to directly map the input slices into high-resolution post-fracturing permeability field. The predicted permeability field is used to calculate probabilistic hydrocarbon volumes inside the filtered P10-P50-P90 stimulated reservoir volumes (SRV), estimate EURs, and further calculate co-developed well...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3725863-MS
... of porosity and permeability. Different machine learning algorithms have been developed including Linear Regression (LR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Adaptive Booster Regressor (AdaBoost), and Support Vector Regression (SVR), to predict...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 26–28, 2021
Paper Number: URTEC-2021-5659-MS
... intelligence seismic data neural network information midland basin basin well logging impedance prediction log analysis upstream oil & gas training data central basin platform earth model building neuron well location automated machine learning framework mlp permian basin URTeC: 5659...

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