Formulations for a three-phase, fully implicit, parallel, EOS compositional surfactant–polymer flooding simulator

Najafabadi, Nariman Fathi; Delshad, Mojdeh; Han, Choongyong; Sepehrnoori, Kamy

doi:10.1016/j.petrol.2012.03.025

Cited by 28 publications

(18 citation statements)

References 10 publications

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“…Theoretically, the use of alkali aims to generate an in-situ surfactant/soap by reacting with the natural acids in oil in order to reduce the IFT between O-W, which makes the removal of crude oil easier thereafter [32,33]. Furthermore, the penetration of the alkaline solution into the crude oil causes the formation of a W/O emulsion, thereby reducing the mobility of the displacing fluids and diverting the injected fluids into the unswept region, resulting in an improvement in the sweep efficiency [34].…”

Section: Flooding Strategiesmentioning

confidence: 99%

Chemical Flooding in Heavy-Oil Reservoirs: From Technical Investigation to Optimization Using Response Surface Methodology

Van

Chon

2016

Energies

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Heavy-oil resources represent a large percentage of global oil and gas reserves, however, owing to the high viscosity, enhanced oil recovery (EOR) techniques are critical issues for extracting this type of crude oil from the reservoir. According to the survey data in Oil & Gas Journal, thermal methods are the most widely utilized in EOR projects in heavy oil fields in the US and Canada, and there are not many successful chemical flooding projects for heavy oil reported elsewhere in the world. However, thermal methods such as steam injection might be restricted in cases of thin formations, overlying permafrost, or reservoir depths over 4500 ft, for which chemical flooding becomes a better option for recovering crude oil. Moreover, owing to the considerable fluctuations in the oil price, chemical injection plans should be employed consistently in terms of either technical or economic viewpoints. The numerical studies in this work aim to clarify the predominant chemical injection schemes among the various combinations of chemical agents involving alkali (A), surfactant (S) and polymer (P) for specific heavy-oil reservoir conditions. The feasibilities of all potential injection sequences are evaluated in the pre-evaluation stage in order to select the most efficient injection scheme according to the variation in the oil price which is based on practical market values. Finally, optimization procedures in the post-evaluation stage are carried out for the most economic injection plan by an effective mathematic tool with the purpose of gaining highest Net Present Value (NPV) of the project. In technical terms, the numerical studies confirm the predominant performances of sequences in which alkali-surfactant-polymer (ASP) solution is injected after the first preflushing water whereby the recovery factor can be higher than 47%. In particular, the oil production performances are improved by injecting a buffering viscous fluid right after the first chemical slug rather than using a water slug in between. The results of the pre-evaluation show that two sequences of the ASP group have the highest NPV corresponding to the dissimilar applied oil prices. In the post-evaluation, the successful use of response surface methodology (RSM) in the estimation and optimization procedures with coefficients of determination R 2 greater than 0.97 shows that the project can possibly gain 4.47 $MM at a mean oil price of 46.5 $/bbl with the field scale of a quarter five-spot pattern. Further, with the novel assumption of normal distribution for the oil price variation, the chemical flooding sequence of concurrent alkali-surfactant-polymer injection with a buffering polymer solution is evaluated as the most feasible scheme owing to the achievement of the highest NPV at the highly possible oil price of 40-55 $/bbl compared to the other scheme.

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Section: Flooding Strategiesmentioning

confidence: 99%

Chemical Flooding in Heavy-Oil Reservoirs: From Technical Investigation to Optimization Using Response Surface Methodology

Van

Chon

2016

Energies

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“…Since the 1970's publications of articles based on different simulation methods in chemical flooding has been done. Some of the articles include a onedimensional and compositional chemical flooding simulator to study additional oil recovery due to different conditions (Pope GA, 1978), study of chemical flooding using an economical model simulator (AJ, 1996), simulator for compositional chemical flooding for surfactant polymer flooding (Han C, 2007) and a vital one in surfactant flooding which is the surfactant phase behavior simulator (Najafabadi et al, 2012).…”

Section: Starsmentioning

confidence: 99%

Numerical Approach for Enhanced Oil Recovery with Surfactant Flooding using STARS (CMG)

2017

International Journal of Petroleum and Petrochemical Engineerin

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“…This problem can be tackled principally by using a polymer that makes the solution more viscous, thereby achieving the proper mobility control and significantly improving the sweep efficiency [27,28]. On the other hand, a high viscosity solution has decreased injectivity and consequently this results in a slower response at the producing well compared to the case where non-viscous fluids are injected [29].…”

Section: Chemical Injection Designmentioning

confidence: 99%

Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

Van

Chon

2016

Energies

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Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV) of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter five-spot scale. Basically, the ANN model generated by this work can be flexibly applied in different economic conditions and extended to a larger reservoir scale for similar chemical flooding projects that demand a quick prediction rather than a simulation process.

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Formulations for a three-phase, fully implicit, parallel, EOS compositional surfactant–polymer flooding simulator

Cited by 28 publications

References 10 publications

Chemical Flooding in Heavy-Oil Reservoirs: From Technical Investigation to Optimization Using Response Surface Methodology

Chemical Flooding in Heavy-Oil Reservoirs: From Technical Investigation to Optimization Using Response Surface Methodology

Numerical Approach for Enhanced Oil Recovery with Surfactant Flooding using STARS (CMG)

Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

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