Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Ng, Cuthbert Shang Wui; Ghahfarokhi, Ashkan Jahanbani; Amar, Menad Nait

doi:10.1007/s13202-021-01199-x

Cited by 24 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…∆t i refers to the timestep difference between the time i and the previous timestep. Such an optimization problem resonates with some of our previous works [8,9]. However, one of the distinctive differences pertains to the number of optimization variables (decision variables) included.…”

Section: Proxy Modeling and Optimization Problemsupporting

confidence: 56%

“…Regarding the selection of mathematical algorithms, nature-inspired algorithms, specifically the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), were preferred due to their structural simplicity and successful implementation in several articles [8,9,14]. These algorithms are derivative-free, implying that computation or approximation of gradient is unnecessary.…”

Section: Algorithmsmentioning

confidence: 99%

“…Therefore, ML-based proxy models are suggested to mitigate the computational challenges. Several previous works [8,9] have established a methodology in this context. Nonetheless, the efficiency of the methodology in resolving the optimization problem with higher dimensionality still requires improvement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Well Control Optimization with Two-Stage Proxy Modeling

Ghahfarokhi

Wiranda

2023

Energies

Self Cite

View full text Add to dashboard Cite

Waterflooding is one of the methods used for increased hydrocarbon production. Waterflooding optimization can be computationally prohibitive if the reservoir model or the optimization problem is complex. Hence, proxy modeling can yield a faster solution than numerical reservoir simulation. This fast solution provides insights to better formulate field development plans. Due to technological advancements, machine learning increasingly contributes to the designing and building of proxy models. Thus, in this work, we have proposed the application of the two-stage proxy modeling, namely global and local components, to generate useful insights. We have established global proxy models and coupled them with optimization algorithms to produce a new database. In this paper, the machine learning technique used is a multilayer perceptron. The optimization algorithms comprise the Genetic Algorithm and the Particle Swarm Optimization. We then implemented the newly generated database to build local proxy models to yield solutions that are close to the “ground truth”. The results obtained demonstrate that conducting global and local proxy modeling can produce results with acceptable accuracy. For the optimized rate profiles, the R2 metric overall exceeds 0.96. The range of Absolute Percentage Error of the local proxy models generally reduces to 0–3% as compared to the global proxy models which has a 0–5% error range. We achieved a reduction in computational time by six times as compared with optimization by only using a numerical reservoir simulator.

show abstract

Section: Proxy Modeling and Optimization Problemsupporting

confidence: 56%

Section: Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Fast Well Control Optimization with Two-Stage Proxy Modeling

Ghahfarokhi

Wiranda

2023

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides that, a study also demonstrated how an ANN could be employed to build a proxy model for waterflooding optimization in a fractured reservoir model [17]. Thereafter, this study was extended to improve the methodology as discussed in [18].…”

Section: Previous Study On Proxy Modelingmentioning

confidence: 94%

Proxy Model Development for the Optimization of Water Alternating CO2 Gas for Enhanced Oil Recovery

Matthew

Ghahfarokhi

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle lengths were assessed to maximize the oil recovery and CO2 stored in the reservoir. One model represents a simple geological model (the Egg Model), while the other represents a complex model (the Gullfaks Model). In this study, the good performance of the proxy models generated accurate results that could be improved by increasing the amount of sampling and segmenting the behavior of the reservoir model (depending on the complexity of the reservoir model). The developed proxies have an average error of less than 2% (compared with simulation results) and are concluded to be robust based on the blind test results. It has also been found that to reach the maximum oil recovery using CO2-WAG, the maximum gas injection rate with the minimum water injection rate is required. However, this configuration may result in a reduction in the total CO2 stored in the reservoir.

show abstract

“…This is achieved through using sensors and ML models. In reservoir engineering, combining ML techniques with data analytics has various benefits [14]. The studies show that this combination can help predict the bottom-hole pressure, optimize water flooding, and forecast hydrocarbon production.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Oil Production through Linear Regression Model and Big Data Tools

Alharbi¹,

Alageel²,

Alsayil³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Fossil fuels, including oil, are the most important sources of energy. They are commonly used in various forms of commercial and industrial consumption. Producing oil is a complex task that requires special management and planning. This can result in a serious problem if the oil well is not operated properly. Oil engineers must have the necessary knowledge about the well's status to perform their duties properly. This study proposes a linear regression method to predicate the oil production value. It takes into account various independent variables, such as the pressure, downhole temperature, and pressure tubing. The proposed method can accurately reach a very close prediction of the actual production value by achieving very interesting results at the end of this study.

show abstract

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Cited by 24 publications

References 60 publications

Fast Well Control Optimization with Two-Stage Proxy Modeling

Fast Well Control Optimization with Two-Stage Proxy Modeling

Proxy Model Development for the Optimization of Water Alternating CO2 Gas for Enhanced Oil Recovery

Prediction of Oil Production through Linear Regression Model and Big Data Tools

Contact Info

Product

Resources

About