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Abstract In this paper, we evaluate optimization techniques to develop, or support, business cases for Intelligent or Smart Wells. A commercial reservoir simulation platform and two reservoir models based on published work are used. Recommendations are made on which methods are most appropriate for large or small numbers of flow control valves (FCVs), available computing power and other parameters. Optimization techniques are categorized as either Closed Loop or Model Based. Closed Loop or Reactive methods respond to specific, measured properties such as water-cut or gas-oil ratio, by opening or closing downhole flow control valves. Model-Based methods use reservoir models to determine the optimal set of flow control valve positions versus time. They can, therefore, behave in a defensive or proactive manner to delay the production of unwanted fluids, as well as a reactive manner, to choke back sections of the well producing unwanted fluids. In this paper, Closed-Loop and Model-Based methods are compared in terms of computational cost. A simple procedure for defining the constraints used in the optimization process is proposed. The procedure is shown to increase the efficiency of the optimization process significantly.
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Flow control equipment (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (0.93)
Abstract The Formation Rate Analysis (FRA) method applies the multivariate linear regression method to invert pressure test data to estimate formation parameters. This method is simple and works well if the measured pressure transient noise is small. However, if the noise is large, errors in the estimated formation parameters are unacceptably large. To solve this problem, we present a multi-scale scatter search optimization method. The basic idea of this optimization method is to iteratively generate sampling points using Hammersley random sequences in regions of various sizes in the parameter space. A reference data set is generated from the candidate optimal solutions and then used to filter the generated sampling points. The uncertainty of the calibrated formation parameters is quantified based on the searched valid optimal solutions. We test this algorithm with formation test data from low permeability, high permeability, and tight formations. Its accuracy, efficiency, and reliability are validated. We also compare it with the original FRA method. The test results show that:compared with the linear regression method, this algorithm more accurately estimates the formation parameters, particularly for the tight formation; compared with the direct Monte Carlo method, this new algorithm more efficiently provides calibrated formation parameters and quantifies more accurately the associated uncertainties; for high permeability formations, the estimated uncertainty of the calibrated formation pressure is very small; for tight formations, flow-line fluid compressibility can be effectively calibrated. However, the short length of the measured pressure transient results in a large uncertainty in the estimated formation pressure and mobility. Also, the mean value of the formation pressure is underestimated if the pressure transient is far from being stabilized. Introduction Formation pressure and mobility are important formation parameters for reservoir engineers. These parameters can be obtained from formation pressure testing. Formation pressure and mobility can be analyzed with the conventional techniques of the pressure transient analysis (PTA), including log-log plots and typed-curve analysis. They can be more accurately determined with the modern PTA analysis technique (Houzé et al., 2007), such as deconvolution and pressure matching. Formation Rate Analysis (FRA™) provides a physical model (Samaha et al., 1996; Kasap et al., 1999) for performing pressure matching to calibrate the formation parameters with the optimization technique. The FRA model was first introduced by Kasap et al. for formation test data analysis. Essentially, it is a mass conservation equation of the fluid in the flow line of a formation tester. It is based on modeling the storage effect of the fluid in flow line, instead of using the drawdown rate as the formation rate. Not only is it used to estimate the formation parameters, but it is also applied to the quality control of the LWD technique (Meister et al., 2003), and to optimize pressure tests while drilling (Lee et al., 2005). The validation of the FRA model is based on the validation of its model assumptions, i.e. spherical flow near the probe, single phase flow of the fluid of constant compressibility, and homogeneous formation. However, formation tests may not be so ideal. Typically, there may be gauge measurement errors, non-Darcy flow effects, supercharging problems, mud filtrate invasion problems, probe sealing problems, mechanical vibration of the piston, etc. All these problems can introduce measured noise and errors into the pressure transient data.
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Formation test analysis (e.g., wireline, LWD) (1.00)
An Efficient Bi-Objective Optimization Workflow Using the Distributed Quasi-Newton Method and Its Application to Well-Location Optimization
Wang, Yixuan (Shell Exploration and Production Company) | Alpak, Faruk (Shell International Exploration and Production Inc. (Corresponding author)) | Gao, Guohua (Shell Global Solutions US Inc.) | Chen, Chaohui (Shell Exploration and Production Company) | Vink, Jeroen (Shell Global Solutions International BV) | Wells, Terence (Shell Global Solutions International BV) | Saaf, Fredrik (Shell Global Solutions US Inc.)
Summary Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly. The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallel information-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration. The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems. To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.
- Europe (1.00)
- North America > United States > California (0.28)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
An Efficient Bi-Objective Optimization Workflow Using the Distributed Quasi-Newton Method and Its Application to Well-Location Optimization
Wang, Yixuan (Shell Exploration and Production Company) | Alpak, Faruk (Shell International Exploration and Production Inc. (Corresponding author)) | Gao, Guohua (Shell Global Solutions US Inc.) | Chen, Chaohui (Shell Exploration and Production Company) | Vink, Jeroen (Shell Global Solutions International BV) | Wells, Terence (Shell Global Solutions International BV) | Saaf, Fredrik (Shell Global Solutions US Inc.)
Summary Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly. The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallelinformation-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration. The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems. To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.
- Europe (1.00)
- North America > United States > California (0.28)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
Abstract In this study we develop an integrated asset model (IAM) for a greenfield offshore oil development and demonstrate its use in an uncertainty analysis workflow. The proposed framework enables a systematic quantification of the uncertainties and provides users with an in-depth understanding of the impact of uncertainties on major design and operational decisions. The IAM is specified for a hypothetical deepwater field to investigate three decisions: optimal initial facility capacity, optimal number of pre-drilled wells, and the optimal number of drilling rigs. The uncertainty analysis addresses two critical variables: reservoir thickness and the transmissibility between the reservoir compartments. This work develops and demonstrates a fast-solving physics-based integrated optimization model where production, drilling, and facility expansion decisions are endogenous (that is, the model solves for these variables implicitly) and thus provides higher quality (and faster) guidance in many cases than the design-of-experiments and response surface workflows currently being used in the oil and gas industry.
- Europe (0.93)
- North America > United States > Texas (0.29)