Development and Application of a New Well Pattern Optimization Algorithm for Optimizing Large Scale Field Development

2009 ◽  
Author(s):  
Jerome Emeka Onwunalu ◽  
Louis J. Durlofsky
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Field Development ◽  
Well Pattern ◽  
Scale Field ◽  
Large Scale Field ◽  
Well Pattern Optimization

Large Scale Field Development Optimization Using High Performance Parallel Simulation and Cloud Computing Technology

2018 ◽  
Author(s):  
Shusei Tanaka ◽  
Zhenzhen Wang ◽  
Kaveh Dehghani ◽  
Jincong He ◽  
Baskar Velusamy ◽  
...  
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Simulation ◽  
Computing Technology ◽  
Field Development ◽  
Scale Field ◽  
Large Scale Field

Smart Well Pattern Optimization Using Gradient Algorithm

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Liming Zhang ◽  
Kai Zhang ◽  
Yuxue Chen ◽  
Meng Li ◽  
Jun Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Control ◽  
Optimization Procedure ◽  
Transformation Method ◽  
Economic Benefits ◽  
Gradient Algorithm ◽  
Generation Process ◽  
Field Development ◽  
Well Pattern ◽  
Well Pattern Optimization

For a long time, well pattern optimization mainly relies on human experience, numerical simulations are used to test different development plans and then a preferred program is chosen for field implementation. However, this kind of method cannot provide suitable optimal well pattern layout for different geological reservoirs. In recent years, more attentions have been paid to propose well placement theories combining optimization algorithm with reservoir simulation. But these theories are mostly applied in a situation with a small amount of wells. For numerous wells in a large-scale reservoir, it is of great importance to pursue the optimal well pattern in order to obtain maximum economic benefits. The idea in this paper is originated from the idea presented by Onwunalu and Durlofsky (2011, “A New Well-Pattern-Optimization Procedure for Large-Scale Field Development,” SPE J., 16(3), pp. 594-607), which focuses on well pattern optimization, and the innovations are as follows: (1) Combine well pattern variation with production control to get the optimal overall development plan. (2) Rechoose and simplify the optimization variables, deduce the new generation process of well pattern, and use perturbation gradient to solve mathematical model in order to ensure efficiency and accuracy of final results. (3) Constrain optimization variables by log-transformation method. (4) Boundary wells are reserved by shifting into boundary artificially to avoid abrupt change of objective function which leads to a nonoptimal result due to gradient discontinuity at reservoir edge. The method is illustrated by examples of homogeneous and heterogeneous reservoirs. For homogeneous reservoir, perturbation gradient algorithm yields a quite satisfied result. Meanwhile, heterogeneous reservoir tests realize optimization of various well patterns and indicate that gradient algorithm converges faster than particle swarm optimization (PSO).

Development and Application of a Coupling Method for Well Pattern and Production Optimization in Unconventional Reservoirs

2019 ◽  
Vol 29 (07) ◽  
pp. 2050105
Author(s):  
Yukun Chen ◽  
Hui Zhao ◽  
Qi Zhang ◽  
Yuhui Zhou ◽  
Hui Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Production Optimization ◽  
Coupling Method ◽  
Gradient Algorithm ◽  
Unconventional Reservoirs ◽  
Geometric Transformation ◽  
Field Development ◽  
Well Pattern ◽  
Well Pattern Optimization

Numerous optimization variables cause the optimization of large-scale field development challenging, which can be overcome by constraining wells to be within patterns and optimizing the parameters relevant to the pattern type and geometry. In this study, a new method incorporating well pattern optimization and production optimization for unconventional reservoirs is presented. By defining a quantitative well pattern description approach, we develop the geometric transformation parameters to quantify well pattern operations (e.g., rotation, shear, especially translation) to change the geometric shape of well patterns including five-spot, inverse seven-spot and inverse nine-spot well pattern. In contrast, a variety of optimization algorithms can be applied to accomplish the optimization of well pattern problems but the computational cost is large for many algorithms. Therefore, we also propose a general upscaling stochastic approximation algorithm (GUSA), which is an improved approximate perturbation gradient algorithm, to realize the combination of well pattern optimization and production optimization simultaneously. It is proved that both the gradient formulation of SPSA algorithm and EnOpt algorithm are the special form of the general approximate perturbation gradient. Afterwards, the synthetic cases (homogeneous and heterogeneous models) and actual unconventional field cases are discussed based on the three mentioned well pattern types. The detailed optimization results show that the presented coupling method can achieve the optimization by transforming well pattern geometry, reducing the total number of wells and adjusting the field injection rate, which is proved to be effective. In sum, this coupling method provides an efficient optimization procedure combing the well pattern optimization and production optimization for practical field development.

A New Well-Pattern-Optimization Procedure for Large-Scale Field Development

SPE Journal ◽  
2011 ◽  
Vol 16 (03) ◽  
pp. 594-607 ◽  
Author(s):  
J.E.. E. Onwunalu ◽  
L.J.. J. Durlofsky
Keyword(s):  
Large Scale ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Second Phase ◽  
Field Development ◽  
Well Pattern ◽  
Multiple Reservoir ◽  
Well Pattern Optimization

Summary The optimization of large-scale multiwell field-development projects is challenging because the number of optimization variables and the size of the search space can become excessive. This difficulty can be circumvented by considering well patterns and then optimizing parameters associated with the pattern type and geometry. In this paper, we introduce a general framework for accomplishing this type of optimization. The overall procedure, which we refer to as well-pattern optimization (WPO), includes a new well-pattern description (WPD) incorporated into an underlying optimization method. The WPD encodes potential solutions in terms of pattern types (e.g., five-spot, nine-spot) and pattern operators. The operators define geometric transformations (e.g., stretching, rotating) quantified by appropriate sets of parameters. It is the parameters that specify the well patterns and the pattern operators, along with additional variables that define the sequence of operations, that are optimized. A technique for subsequent well-by-well perturbation (WWP), in which the locations of wells within each pattern are optimized, is also presented. This WWP represents an optional second phase of WPO. The overall optimization procedure could be used with a variety of underlying optimization methods. Here, we combine it with a particle-swarm-optimization (PSO) technique because PSO methods have been shown recently to provide robust and efficient optimizations for well-placement problems. Detailed optimization results are presented for several example cases. In one case, multiple reservoir models are considered to account for geological uncertainty. For all examples, significant improvement in the objective function is observed as the algorithm proceeds, particularly at early iterations. The use of well-by-well perturbation (following determination of the optimal pattern) is shown to provide additional improvement. Limited comparisons with results using standard well patterns of various sizes demonstrate that the net present values (NPVs) achieved by the new algorithm are considerably larger. Taken in total, the optimization results highlight the potential of the overall procedure for use in practical field development.

Biodata response elaboration: A large-scale field experiment

2010 ◽  
Author(s):  
Julia Levashina ◽  
Frederick P. Morgeson ◽  
Michael A. Campion
Keyword(s):  
Field Experiment ◽  
Large Scale ◽  
Scale Field ◽  
Large Scale Field
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