A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs

Zhang, Yanbin; He, Jiankui; Yang, Changdong; Xie, Jiang; Fitzmorris, Robert; Wen, Xian‐Huan

doi:10.2118/191126-pa

Cited by 20 publications

(4 citation statements)

References 32 publications

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“…Nevertheless, ref. [89] stated that data-driven models fail to accurately depict the fundamental principles of the basin's physics as it evolves, and their parameters rely on the specific range of data employed for calibration [89][90][91].…”

Section: Data-driven Modelsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

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Section: Data-driven Modelsmentioning

confidence: 99%

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Byaruhanga,

Kibirige,

Gokool

et al. 2024

Water

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“…Mixed physics-based, data-driven approaches are also starting to be proposed in this field, see e.g. [88,89,90]. In the case of earthquake engineering, Song et al [91] give data-driven computer codes for accurately predicting the performance of buildings from raw databases.…”

Section: Gas and Oil Industrymentioning

confidence: 99%

Data-driven modeling and learning in science and engineering

Montáns

Chinesta

Gómez‐Bombarelli

et al. 2019

Comptes Rendus. Mécanique

185

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“…Our work is focused on O&G forecasting based on ML techniques; in this sense, several methods can be applied: probabilistic DCA with Markov-chain Monte Carlo simulation (MCMC) to forecast the production in liquid-rich shale and gas-shale wellbores [17]; radial basis function networks (RBF) are common to estimate the gas flow rate in multiphase production wellbores [18]; ANN based on back-propagation (BP) networks can be optimized by fuzzy clustering and genetic algorithms to improve the accuracy of the forcasting of the crude oil production [19]; Bayesian networks (BN) predict wellbore signatures to forecast the oil production rate [20]; physics-based and data-driven diffusive diagnostic function (DDF) for history matching, prediction, and characterization of reservoirs [21]; probabilistic forecasting to estimate prediction intervals on a large oil production dataset [22]; Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM), RBF, Multilayer Perceptron (MLP), and Gene Expression Programming (GEP) are also evaluated to determine the orificemeter flow rate [23].…”

Section: Introductionmentioning

confidence: 99%

The Golem: A General Data-Driven Model for Oil & Gas Forecasting Based on Recurrent Neural Networks

Martínez

Rocha

2023

IEEE Access

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Oil & gas forecasting is one of the most critical issues in reservoir management. Physics-based simulations are the most common models used for production forecasts in oilfields. Previous works based on Machine Learning (ML) developed models focused on the oil rate as unique target variable, a forecasting by one-day output , and just one class of reservoir (synthetic or actual). This work introduces a general data-driven model based on Recurrent Neural Networks to forecast an adaptive sequence of timestamps for the complete production rates (oil, gas, and water), and we also included the wellbore pressure as target variable, for both classes of reservoirs as actual as synthetic. The first dataset was obtained from the synthetic benchmark UNISIM-II-H, which simulates a carbonate reservoir in the Brazilian pre-salt; the second dataset is from an actual reservoir, the Volve oilfield, a decommissioned reservoir in the Norwegian North Sea. The forecasting is calculated using an input sequence of daily values from the historical record of the production rates and the pressure; the output is also a set of the values to the next sequence of days for one selected production variable (oil, gas, water, or pressure). The size of both input and output sets is adaptive and its adjustment depends on the dataset size and the production time. We built the model and compared it between the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) implementations. We tuned the architectural parameters of the model, the input size of historical records, and the output forecasting days. We performed the training/testing procedures with several sizes for the training dataset from the target wellbore and tested with the remaining data to evaluate the model stability. We adopted the Symmetric Mean Absolute Percentage Error (SMAPE) and the coefficient of determination r-square (R 2 ) metrics to compare our forecasting values to the production rates and the pressure, most of the results for both synthetic and actual oilfields exhibited that the model can follow an accurate trend of the production rates and the pressure; and the output values are approximated. Forecasting values from the designed model exhibited closer values when compared to the expected data from the wellbores in most of the experiments, some cases exhibited an SMAPE lower than 2 and R 2 up to 0.99. The model can learn the behavior of each production variable according to the training set and the forecasting output can be adapted for a set of several timestamps.

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A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs

Cited by 20 publications

References 32 publications

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Data-driven modeling and learning in science and engineering

The Golem: A General Data-Driven Model for Oil & Gas Forecasting Based on Recurrent Neural Networks

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