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Keywords: prediction
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3857308-MS
... provide poor predictions for the near-critical/volatile fluids encountered in many unconventional reservoirs ( e.g. , highly volatile oils, retrograde gas condensates, and wet gases). In this work we present a series of customized PVT correlations to address these deficiencies for unconventional...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3854193-MS
... rates (oil, gas, and water) and pressures are typically recorded on an hourly basis. Therefore, even though flowback may only take a few weeks, there are hundreds of available data points. The authors have developed an innovative approach to predict short-term cumulative oil production and long-term oil...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3860762-MS
... efficiency. Using physics-based models, observed trends were captured with a maximum of 10% error in the prediction. In this collaborative work, cluster efficiency was studied to see the impacts on both near-wellbore and farfield fracturing dynamics. The availability of multiple types of downhole data...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3852016-MS
... development campaign in the Haynesville to test the suitability of the model to represent well performance and its ability to predict production with a very limited data history calibration. Additionally, several wells exhibited parent-child effects, so FD-RTA was also applied to quantify the change...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3863926-MS
... Abstract Predicting production behavior plays an important role in oil and gas production and aids engineers to perform field management. However, this can be challenging in heterogeneous reservoirs using traditional models that require expensive computational time and various types...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864551-MS
... Abstract Accurate pore pressure and stress estimation are extremely important for safe and efficient drilling. The objective of this study is to establish robust models for an unconventional shale play to predict pore pressure and stress magnitudes from elastic properties derived from seismic...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3864002-MS
... control selection esp unconventional resource technology conference equation artificial lift timing prediction unconventional reservoir production logging well performance artificial lift system alt workflow seg unconventional resource technology conference paper gas injection reservoir...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3870467-MS
... units (DSUs) containing parent wells is hindered by the large degree of uncertainty around child well performance. Infill child wells have been observed with up to a 40% degradation from their unbounded parents in the Midland Basin. We present an AI enabled workflow to predict infill well performance...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3870070-MS
.... The developed data were used to train different Artificial Neural Network (ANN) algorithms. ANN algorithms showed high capabilities to develop a surrogate model of the high-fidelity model. The ANN models provided robust predictions on the monthly production level for oil recovery factors. The robustness...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 13–15, 2023
Paper Number: URTEC-3871063-MS
... Abstract Flow regime changes are important to capture and predict when estimating the future production from tight oil wells. Due to the nature of hydrofracture completions and reservoir heterogeneity, flow regimes are difficult to predict before they are observed. Also, while some wells...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3702980-MS
... reservoir characterization deep learning complex reservoir artificial intelligence upstream oil & gas machine learning neural network accuracy repeatability rf hybrid xgb xgb hybrid powder river basin prediction workflow model result petrophysicist xgb xgb hybrid best result...
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
... machine learning us government upstream oil & gas production forecasting deep learning modeling & simulation neural network artificial intelligence complex reservoir physics-guided deep learning prediction field dataset application dataset equation pgdl model experiment...

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