Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Torrado, Ruben Rodriguez; Echeverría-Ciaurri, David; Mello, Ulisses T.; Droz, Sonia Embid

doi:10.2118/166392-pa

Cited by 12 publications

(5 citation statements)

References 29 publications

(36 reference statements)

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“…Further testing with realistic 3D models should also be performed. Finally, comparisons with other heuristic procedures for determining well allocation/rates (such as those in [34,14]) should be performed. Optimization results using the various approaches could then be assessed, and these findings could be used to devise a hybrid implementation based on the most promising components from the different methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…Our two-step ST somewhat resembles the procedure developed by Rodríguez Torrado et al [34] and Embid Droz et al [35] for setting well controls. Their methodology also relies on the optimization of a single overall field rate variable (e.g., field oil production rate), and was used in combination with well scheduling and location optimization procedures.…”

Section: Introductionmentioning

confidence: 99%

“…Well controls with this approach are determined through estimation of the field recovery factor, while the field rate variable is obtained using derivative-free methods. A key difference between our two-step ST and the procedure for optimizing the well controls developed by Rodríguez Torrado et al [34] is in the (heuristic) scheme used to determine the well-rate ratios. In our approach, a simplified flow problem is considered for the determination of the wellrate ratios, while their procedure uses static reservoir properties.…”

Section: Introductionmentioning

confidence: 99%

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Well control optimization using a two-step surrogate treatment

Brito

Durlofsky

2020

Journal of Petroleum Science and Engineering

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Large numbers of flow simulations are typically required for the determination of optimal well settings. These simulations are often computationally demanding, which poses challenges for the optimizations. In this paper we present a new two-step surrogate treatment (ST) that reduces the computational expense associated with well control optimization. The method is applicable for oil production via waterflood, with well rates optimized at a single control period. The two-step ST entails two separate optimizations, which can both be performed very efficiently. In the first optimization, optimal well-rate ratios (i.e., the fraction of total injection or production associated with each well) are determined such that a measure of velocity variability over the field is minimized, leading to more uniform sweep. In the second step, overall injection and production rates are determined. The flow physics in the first step is highly simplified, while the actual physical system is simulated in the second step. Near-globally-optimal results can be determined in both cases, as the first optimization is posed as a QP problem, and the second step entails just a single optimization variable. Under full parallelization, the overall elapsed time for the ST corresponds to the runtime for 1-2 full-order simulations. Results are presented for multiple well configurations, for 2D and 3D channelized models, and comparisons with formal optimization procedures (mesh adaptive direct search or MADS, and an adjoint-gradient method) are conducted. Three different fluid mobility ratios (M = 1, 3 and 5) are considered. Optimization results demonstrate that the two-step ST provides results in reasonable agreement with those from MADS and adjoint-gradient methods, with speedups of 5× or more. We also show that the ST is applicable in the inner-loop in field development optimization, where it will be especially useful since many different well configurations must be evaluated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Well control optimization using a two-step surrogate treatment

Brito

Durlofsky

2020

Journal of Petroleum Science and Engineering

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“…Mahjour et al (2020a) also introduced another pairwise similarity distance for flow unit models using the simple matching coefficient. Furthermore, the scenario reduction techniques using static features are accepted as an applicable and user-friendly method in other research works (Mahjour et al 2020b;Torrado et al 2015;Armstrong et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Mahjour

Santos

Correia

et al. 2021

J Petrol Explor Prod Technol

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The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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“…The literature in Field Development Plan (FDP) optimization under uncertainty is quite profuse and mainly based on decision trees, data assimilation frameworks and optimization algorithms. Worth citing the work on asset evaluation and decision making from (Hegstad et al 2004), (Evans 2005), (Saputelli et al 2007), and in terms of optimization under uncertainty the previous work of (De Paola et al, 2018) and (Rodriguez-Torrado et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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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Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Cited by 12 publications

References 29 publications

Well control optimization using a two-step surrogate treatment

Well control optimization using a two-step surrogate treatment

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Reinforcement Learning For Field Development Policy Optimization

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