A comprehensive workﬂow for real time injection-production optimization based on equilibrium displacement

“…However, the definitions and standards of balanced displacement differ greatly, including water breakthrough at the same time, reaching the same water cut or oil saturation or oil recovery at each connecting oil well, etc. (Li, 2003 [28]; Feng, 2016 [29]; Cui, 2017 [30]; Chang, 2019 [31]; Chen, 2020 [32]).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Optimizing Water-Flooding Strategies in Multi-Layer Sandstone Reservoirs

Guo,

Yang,

Zhao

et al. 2024

Energies

View full text Add to dashboard Cite

As one of the most important and economically enhanced oil-recovery technologies, water flooding has been applied in various oilfields worldwide for nearly a century. Stratified water injection is the key to improving water-flooding performance. In water flooding, the water-injection rate is normally optimized based on the reservoir permeability and thickness. However, this strategy is not applicable after oilfields enter the ultra-high-water-cut period. In this study, an original method for optimizing water-flooding parameters for developing multi-layer sandstone reservoirs in the entire flooding process and in a given period is proposed based on reservoir engineering theory and optimization technology. Meanwhile, optimization mathematical models that yield maximum oil recovery or net present value (NPV) are developed. The new method is verified by water-flooding experiments using Berea cores. The results show that using the method developed in this study can increase the total oil recovery by approximately 3 percent compared with the traditional method using the same water-injection amounts. The experimental results are consistent with the results from theoretical analysis. Moreover, this study shows that the geological reserves of each layer and the relative permeability curves have the greatest influence on the optimized water-injection rate, rather than the reservoir properties, which are the primary consideration in a traditional optimization method. The method developed in this study could not only be implemented in a newly developed oilfield but also could be used in a mature oilfield that has been developed for years. However, this study also shows that using the optimized water injection at an earlier stage will provide better EOR performance.

show abstract

“…Over the past decades, a large number of experts and scholars have conducted research on this, which can be summarized into three categories: (1) Based on the reservoir engineering formula method. This method combines the material balance and trend fitting method to establish the production dynamic prediction formula, then calculates the production dynamics of different injection-production schemes, obtains the objective function value (such as net present value and cumulative oil production), and then compares and selects the best [2][3][4]. But because reservoir development is affected by multiple factors, and this method has ideal assumptions, it leads to problems such as poor accuracy of results and narrow method adaptability.…”

Section: Introductionmentioning

confidence: 99%

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Tang

et al. 2021

Geofluids

View full text Add to dashboard Cite

A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

show abstract

A comprehensive workﬂow for real time injection-production optimization based on equilibrium displacement

Cited by 9 publications

References 6 publications

Development theory and practice of water injection induced fractures in ultra-low permeability reservoirs

Development theory and practice of water injection induced fractures in ultra-low permeability reservoirs

A New Method for Optimizing Water-Flooding Strategies in Multi-Layer Sandstone Reservoirs

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Contact Info

Product

Resources

About