Experimental and simulation studies for optimization of water–alternating-gas (CO2) flooding for enhanced oil recovery

Pancholi, Swapnil; Negi, Gudendra Singh; Agarwal, Jatin; Bera, Achinta; Shah, Maunish

doi:10.1016/j.ptlrs.2020.04.004

Cited by 24 publications

(22 citation statements)

References 20 publications

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“…The simulated 512 WAG reservoir models demonstrated that WAG incremental recovery factor is typically between 5 and 15%; however, up to 30% incremental recovery factor was observed from few reservoir models. − …”

Section: Resultsmentioning

confidence: 97%

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

2021

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Predicting the incremental recovery factor with an enhanced oil recovery (EOR) technique is a very crucial task. It requires a significant investment and expert knowledge to evaluate the EOR incremental recovery factor, design a pilot, and upscale pilot result. Water-alternating-gas (WAG) injection is one of the proven EOR technologies, with an incremental recovery factor typically ranging from 5 to 10%. The current approach of evaluating the WAG process, using reservoir modeling, is a very time-consuming and costly task. The objective of this research is to develop a fast and cost-effective mathematical model for evaluating hydrocarbon-immiscible WAG (HC-IWAG) incremental recovery factor for medium-to-light oil in undersaturated reservoirs, designing WAG pilots, and upscaling pilot results. This integrated research involved WAG literature review, WAG modeling, and selected machine learning techniques. The selected machine learning techniques are stepwise regression and group method of data handling. First, the important parameters for the prediction of the WAG incremental recovery factor were selected. This includes reservoir properties, rock and fluid properties, and WAG injection scheme. Second, an extensive WAG and waterflood modeling was carried out involving more than a thousand reservoir models. Third, WAG incremental recovery factor mathematical predictive models were developed and tested, using the group method of data handling and stepwise regression techniques. HC-IWAG incremental recovery factor mathematical models were developed with a coefficient of determination of about 0.75, using 13 predictors. The developed WAG predictive models are interpretable and user-friendly mathematical formulas. These developed models will help the subsurface teams in a variety of ways. They can be used to identify the best candidates for WAG injection, evaluate and optimize the WAG process, help design successful WAG pilots, and facilitate the upscaling of WAG pilot results to full-field scale. All this can be accomplished in a short time at a low cost and with reasonable accuracy.

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Section: Resultsmentioning

confidence: 97%

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

2021

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“…In analogy, the efficiency of methods of enhanced oil recovery (EOR) where gas is present also depends on the wetting behavior, but up to four phases (rock-brine-oil-gas) need to be considered depending on the recovery technique in question. To name a few: alternating gas-water flooding [14], carbonated water flooding [4],or a combination of these techniques, e.g. during foam flooding [15].…”

Section: Introductionmentioning

confidence: 99%

Experimental determination of wetting behavior under non-atmospheric conditions relevant to reservoirs: a practical guide

Samara

Jaeger

2022

SN Appl. Sci.

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The wetting behavior in subsurface reservoirs plays a crucial role in storage, migration and liberation of fluid phases that are especially relevant to the energy sector. Further, the three-phase contact angle is important for implementing safe and successful subsurface storage of hydrogen and carbon dioxide. For reliable statements on the in situ wetting, it is important to be aware of the decisive factors of influence in order to design and perform the respective experiments in an appropriate way. This paper discusses the most important effects that shall be considered when determining contact angles experimentally, like drop size, surface roughness, aging process, dynamic behavior, and the pH, giving some valuable guidance to guarantee significant results. A drop base diameter of no less than 5 mm is found to be appropriate to minimize the impact of gravity on the contact angle under reservoir conditions. It is further confirmed that surface roughness contributes to better water wetting when the contact angles are below 90°. The versatility of contact angle measurements is shown through the dual-drop dual-crystal method that can be applied to estimate the adhesion forces present at the rock-brine interface and that need to be overcome by the flooding liquid to effectively displace hydrocarbons from the pores.

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“…Water flooding remains the most frequently used secondary oil recovery method. However, the percentage of original oil in place left after the cessation of water flooding in many reservoir fields is estimated to be as high as 50 -70% [43,34,51]. The reduced performance of water flooding leading to the sizable leftover of oil has been linked to many factors such as the presence of unfavorable mobility ratios (due to heavy oil), high level of heterogeneity (in porosity and permeability), etc., in the reservoir [16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery

Keil¹,

Kleikamp²,

Lorentzen³

et al. 2022

Preprint

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In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE-constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evaluations of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.

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Experimental and simulation studies for optimization of water–alternating-gas (CO2) flooding for enhanced oil recovery

Cited by 24 publications

References 20 publications

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

Fast and Cost-Effective Mathematical Models for Hydrocarbon-Immiscible Water Alternating Gas Incremental Recovery Factor Prediction

Experimental determination of wetting behavior under non-atmospheric conditions relevant to reservoirs: a practical guide

Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery

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