An End-to-End Deep Sequential Surrogate Model for High Performance Reservoir Modeling: Enabling New Workflows

Navrátil, Jǐŕı; Paola, G. De; Kollias, Georgos; Nadukandi, Prashanth; Codas, Andrés; Ibanez-Llano, Cristina

doi:10.2118/201775-ms

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…As noted, the predicted value error is less than 5% against the actual values when using the Long Short-Term Memory algorithm. Surrogate modeling to replace reservoir simulators is described in References [29,30].…”

Section: Introductionmentioning

confidence: 99%

The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

2022

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This paper aims to study the applicability of machine-learning algorithms, specifically neural networks, for forecasting the effectiveness of Improved recovery methods. Radial jet drilling is the case operation in this study. Understanding changes in reservoir flow properties and their effect on liquid flow rate is essential to evaluate the radial jet drilling effectiveness. Therefore, liquid flow rate after radial jet drilling is the target variable, while geological and process parameters have been taken as features. The effect of various network parameters on learning quality has been assessed. As a result, conclusions on the applicability of neural networks to evaluate the radial jet drilling potential of wells in various geological conditions of carbonate reservoirs have been made.

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Section: Introductionmentioning

confidence: 99%

The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

2022

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Reinforcement Learning For Field Development Policy Optimization

Paola

Ibanez-Llano

Ríos³

et al. 2020

Day 4 Thu, October 29, 2020

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What is the next best drilling decision to make in a Field Development Plan (FDP)? This is the key question we address in this work. A FDP consists of a sequence of decisions. Each action we take affects the reservoir and conditions any future decision. The novelty of our proposed approach is the consideration of the sequential nature of the decisions through the framework of Dynamic Programming (DP) and Reinforcement Learning (RL). In this framework, each scheduled drilling decision depends on the observations acquired between drillings. This methodology allows moving the focus from a static Field Development Plan optimization to a more dynamic framework that we call Field Development Policy Optimization. In addition to the formulation of this new framework, we have applied this methodology to optimize the development of a real oil and gas field. We model the FDP optimization problem under subsurface uncertainty as a Partially Observable Markov Decision Process (POMDP) and solve it through a RL algorithm in order to find an optimal drilling policy. Our methodology works for a general reservoir with a given set of geological model representing. To speed up the learning process we utilize a trained Deep Recurrent Neural Network (RNN) to approximate the reservoir simulator flows, which are subsequently used to compute the economic performance of a drilling policy through its discounted cash flows. The RNN is trained and tested on a set of reservoir simulator runs over randomly sampled realizations of our reservoir model, well location, type and control sequences of a drilling plan. From all the possible decisions involved in a FDP, we focus here only on finding optimal adaptive well drilling schedules (locations of vertical wells and well types). The RL agent learns the best drilling schedule policy by generating simulated episodes of experience and iteratively improving the policy using a Q value function approximated by a neural network trained across episodes. The final solution consists of an adaptive Field Development Plan yielding the highest expected Net Present Value (NPV), computed within a given, available time budget. It specifies an adaptive drilling schedule of producer and injector, well locations and well controls, as a function of the information obtained at each drilling step. The methodology has been applied to an actual reservoir for infill well location decisions. In this case, our objective is finding the best well placement and well type for the next producer and injector wells, as well as the optimization of the control schedule for new and preexisting wells in the reservoir. Our results show the learning progress of our RL algorithm until finding the optimal drilling plan. The robustness of the solution is evaluated across the best-trained policies. Methodology and results have been validated using a brute force sampling approach. Both RL and brute force approaches were possible due to our fast-to-compute RNN approximation of the reservoir simulator. This work represents, to our knowledge, the first application of an end-to-end AI workflow for Field Development Policy Evaluation in real fields, based on Reinforcement Learning and Deep Learning. The proposed methodology puts together an optimal field evaluation in planning and a surveillance workflow for a reactive decision-making.

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Long-Term Forecasting and Optimization of Non-Stationary Well Operation Modes Through Neural Networks Simulation

Ponomarev

Vershinin

2021

Day 3 Thu, October 14, 2021

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The article discusses the results of long-term forecasting of non-stationary technological modes of production wells using neural network modeling methods. The main difficulty in predicting unsteady modes is to reproduce the response of producing wells to a sharp change in the mode of one of the wells. Such jumps, as a rule, lead to a rapid increase in the forecast error. Training and forecasting of modes was carried out on the data of numerical hydrodynamic modeling. Two fields with significantly different properties, the number of wells and their modes of operation were selected as objects of modeling. Non-stationarity was set by changing the regime on one or several production wells at different points in time. The LSTM recurrent neural network carried out forecasting of production technological parameters. This made it possible to take into account the time-lagging influence of the wells on each other. It is shown that the LSTM neural network allows predicting unsteady technological modes of well operation with an accuracy of up to 5% for a period of 10 years. The solution of the problem of optimization of oil production is considered on the example of one of the models. It is shown that the optimal solution found by the neural network differs from the solution found by hydrodynamic modeling by 5%. At the same time, a significant gain in calculation time was achieved.

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An End-to-End Deep Sequential Surrogate Model for High Performance Reservoir Modeling: Enabling New Workflows

Cited by 6 publications

References 16 publications

The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

Reinforcement Learning For Field Development Policy Optimization

Long-Term Forecasting and Optimization of Non-Stationary Well Operation Modes Through Neural Networks Simulation

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