Data-driven models for production forecasting and decision supporting in petroleum reservoirs

• URL: http://arxiv.org/abs/2508.18289v1
• Date: Thu, 21 Aug 2025 19:48:58 GMT
• Title: Data-driven models for production forecasting and decision supporting in petroleum reservoirs
• Authors: Mateus A. Fernandes, Michael M. Furlanetti, Eduardo Gildin, Marcio A. Sampaio,
• Abstract summary: This project proposes to deal with this problem through a data-driven approach and using machine learning methods.<n>The objective is to develop a methodology to forecast production parameters based on simple data as produced and injected volumes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting production reliably and anticipating changes in the behavior of rock-fluid systems are the main challenges in petroleum reservoir engineering. This project proposes to deal with this problem through a data-driven approach and using machine learning methods. The objective is to develop a methodology to forecast production parameters based on simple data as produced and injected volumes and, eventually, gauges located in wells, without depending on information from geological models, fluid properties or details of well completions and flow systems. Initially, we performed relevance analyses of the production and injection variables, as well as conditioning the data to suit the problem. As reservoir conditions change over time, concept drift is a priority concern and require special attention to those observation windows and the periodicity of retraining, which are also objects of study. For the production forecasts, we study supervised learning methods, such as those based on regressions and Neural Networks, to define the most suitable for our application in terms of performance and complexity. In a first step, we evaluate the methodology using synthetic data generated from the UNISIM III compositional simulation model. Next, we applied it to cases of real plays in the Brazilian pre-salt. The expected result is the design of a reliable predictor for reproducing reservoir dynamics, with rapid response, capability of dealing with practical difficulties such as restrictions in wells and processing units, and that can be used in actions to support reservoir management, including the anticipation of deleterious behaviors, optimization of production and injection parameters and the analysis of the effects of probabilistic events, aiming to maximize oil recovery.

Related papers

• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models [0.0]
  This study proposes a surrogate-based, data-driven methodology for optimizing complex real-world manufacturing systems.<n>Machine learning models are employed to approximate system behavior and construct surrogate models, which are integrated into a tailored metaheuristic approach.<n>Results show that the surrogate-based optimization approach outperforms historical best configurations.
  arXiv  Detail & Related papers  (2025-07-15T10:52:45Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Machine Learning-Based Completions Sequencing for Well Performance Optimization [0.0]
  The primary goal of this project is to develop effective machine-learning models that can integrate the effects of multidimensional predictive variables. All three models yielded cumulative production predictions with root mean squared error (RMSE) values ranging from 7.35 to 20.01 thousand barrels of oil. There is potential for significant improvement, including comprehensive feature engineering, and a recommendation of exploring the use of hybrid or surrogate models.
  arXiv  Detail & Related papers  (2024-02-23T21:11:17Z)
• A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant [0.0]
  The objective is to increase the prediction performance of an industrial wastewater treatment plant by transferring the knowledge of (i) an open-source simulation model that captures the underlying physics of the process, albeit with dissimilarities to the target plant, and (ii) another industrial plant characterized by noisy and limited data but located in the same refinery, and (iii) the model in (ii) The results have shown that test and validation performance are improved up to 27% and 59%, respectively.
  arXiv  Detail & Related papers  (2024-01-20T11:53:08Z)
• Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering [0.0]
  The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks (BNs) The main result of the work can be considered the development of a methodology for studying the parameters of reservoirs based on BNs.
  arXiv  Detail & Related papers  (2022-04-01T13:23:36Z)
• Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells [9.832272256738452]
  We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells. We generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns.
  arXiv  Detail & Related papers  (2021-10-12T15:00:45Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea [58.720142291102135]
  The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focus on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
  arXiv  Detail & Related papers  (2021-03-03T18:51:46Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.