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Keywords: prediction
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Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222192-MS
... to evaluate microporosity so the permeability can be predicted using this equation. The results for different datasets are discussed later. It uses dry elastic properties, mineralogical volume, EPAR threshold, and pre-computed EPAR (from single porosity standard DEM) as modelling inputs A matrix...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222299-MS
...Data-Driven Methodology This section outlines the process of developing machine learning (ML) models for predicting three key targets from open-hole logs: bulk density (RHOB), neutron porosity (NPHI), and compressional sonic travel time (DTC). Figure 1 present the flowchart to conduct...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-221813-MS
... Abstract Monitoring reservoir pressure with time is an important part of reservoir management. The conventional approach using hydrodynamic modeling to predict reservoir pressure may not always match the surveillance data due to long history matching and model updating process, too large...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-221814-MS
... needs and conditions. These include production data, well logs, and seismic data, among others. This data is utilized to construct a resilient proxy model utilizing machine learning algorithms, leading to precise predictions of field water production. Moreover, these runs are completed in a matter...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-221849-MS
... types of forward-looking models are developed. The inverse model predicts geological characteristics using field pressure data as input. The forward-looking models aim to simulate pressure responses and the evolution of the gas plume. Initially, input data is processed through the inverse model...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222450-MS
... type, offering a comprehensive explanation for the dynamic behavior observed in the field's flank area, where this rock type extends into the upper part of the Lower Shuaiba. The results of the novel application for cementation exponent "m" prediction provided a variable m solution for Archie...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222304-MS
... without refractory repair Each module consists of Prediction block Which predict air, fuel demand for every target temperature based on past & current data Monitoring block This block scans all critical parameter related to dry-out & alert operator if deviation Adaptive control...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222306-MS
.... with application of appropriate weights on each. The PQI used weighting techniques such as the analytic hierarchy process and entropy weight method. Multiple classification and regression algorithms were tested and used to learn from these inputs to predict stage placement and proppant placement success...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222406-MS
... Abstract Efficient forecasting involves generating accurate and reliable predictions while minimizing the use of resources, such as time, training data, and computational power. This study investigates the potential of Fourier Neural Operators (FNOs) combined with transfer learning (FNO+TL...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222903-MS
... The generated synthetic dataset was used to train a neural network model, specifically an MLP. The model was trained on 10,000 records to predict key fracturing design parameters and production outcomes. Performance metrics such as RMSE, MAPE, and the coefficient of determination (R²) were used...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222938-MS
... drilling fluid formulation geologist dip-slip fault neural network wellbore integrity wellbore design machine learning equivalent density knowledge artificial intelligence prediction model reservoir characterization drilling fluid chemistry horizontal principal stress confidence...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-223034-MS
... To improve the prediction accuracy of subtle faults, a new AI subtle faults characterization method based on OBN seismic attributes and fault physical simulation was proposed. The authors thank the China National Petroleum Corporation (CNPC) and Petro-China Exploration and Development Company...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-223049-MS
... Numerical simulation is often used in existing research to generate GOR data. For example, Molokwu proposed an empirical correlation based on statistical evaluation to predict the gas-oil ratio in gas condensate systems ( Molokwu et al., 2018 ). While this method simplifies the black oil model...
Proceedings Papers

Paper presented at the ADIPEC, November 4–7, 2024
Paper Number: SPE-222710-MS
... underground. These datasets, while fundamental, are supplemented by our Physics-regularized Fourier-Integrated Hybrid Deep Neural Framework (PR-F-IHDNF) to enhance predictive capabilities. This deep learning-based surrogate model integrates convolutional LSTM, convolutional neural networks, and Fourier neural...

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