A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs

Shafiei, Ali; Dusseault, Maurice B.; Zendehboudi, Sohrab; Chatzis, Ioannis

doi:10.1016/j.fuel.2013.01.056

Cited by 105 publications

(47 citation statements)

References 42 publications

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“…However, a limited number of successful case histories (partially or marginally so in some cases) or field trials exist for steamflooding in VO NFCRs. All the field pilots of steamflooding in VO NFCRs available in the literature were studied to assemble a database, and as many of the critical parameters as we could were collected or calculated for this study (see Shafiei et al [10] for the database). To enhance the value of statistical analysis, the database was extended to include steamflooding trials in highly (naturally) fractured sandstone viscous oil reservoirs, and some experimental data from similar cases was incorporated as well.…”

Section: Methodsmentioning

confidence: 99%

“…Several researchers have investigated important aspects of steamflooding (e.g., production mechanisms, productivity, wettability effects, process properties, feasibility) in homogeneous and heterogeneous porous media through experimental works, analytical modeling, and numerical modeling/simulations [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Most articles focus on steamflooding in unfractured (single porosity) systems.…”

Section: Introductionmentioning

confidence: 99%

“…Empirical models or correlations have not been yet developed to estimate CSOR and RF for steamflooding in VO NFCRs. However, various models and correlations are reported for performance prediction and analysis of various steam injection processes for VO production from oil sands and other unconsolidated sandstone VO reservoirs [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Some smart models have also been developed and introduced to assess the performance of steamflooding in NFCRs [10].…”

Section: Introductionmentioning

confidence: 99%

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Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs

et al. 2018

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Thermal oil recovery techniques, including steam processes, account for more than 80% of the current global heavy oil, extra heavy oil, and bitumen production. Evaluation of Naturally Fractured Carbonate Reservoirs (NFCRs) for thermal heavy oil recovery using field pilot tests and exhaustive numerical and analytical modeling is expensive, complex, and personnel-intensive. Robust statistical models have not yet been proposed to predict cumulative steam to oil ratio (CSOR) and recovery factor (RF) during steamflooding in NFCRs as strong process performance indicators. In this paper, new statistical based techniques were developed using multivariable regression analysis for quick estimation of CSOR and RF in NFCRs subjected to steamflooding. The proposed data based models include vital parameters such as in situ fluid and reservoir properties. The data used are taken from experimental studies and rare field trials of vertical well steamflooding pilots in heavy oil NFCRs reported in the literature. The models show an average error of <6% for the worst cases and contain fewer empirical constants compared with existing correlations developed originally for oil sands. The interactions between the parameters were considered indicating that the initial oil saturation and oil viscosity are the most important predictive factors. The proposed models were successfully predicted CSOR and RF for two heavy oil NFCRs. Results of this study can be used for feasibility assessment of steamflooding in NFCRs.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs

et al. 2018

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“…Experimental results were analyzed using Design Expert V6 software and a regression model was proposed. Analysis of variance (ANOVA) was performed based on the proposed model to find out the interaction between the variable and the response [21]. The coefficients of determination (R 2 and R Fig.…”

Section: Box-behnken Designmentioning

confidence: 99%

Statistical Optimization for Production of Light Olefins in a Fluidized‐Bed Reactor

Pajaie

Taghizadeh

2015

Chem Eng & Technol

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The methanol-to-olefins reaction (MTO) was studied in a small-scale fluidizedbed reactor over synthesized silicoaluminophosphate (SAPO-34) catalysts. Mesoporous nanocrystalline SAPO-34 molecular sieves were synthesized hydrothermally by ultrasonic and microwave-assisted aging processes in the presence of hexadecyltrimethylammonium bromide (CTAB) and tetradecyldimethyl(3-trimethoxysilylpropyl)ammonium chloride (TPOAC) as surfactant agents. The BoxBehnken experimental design method was applied to determine the optimum operating parameters of this process conducted in the fluidized-bed reactor. The optimum conditions in terms of reaction temperature, ratio of inlet gas velocity to minimum fluidizing velocity, and MeOH weight fraction were evaluated.

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“…Thermal methods are suitable for heavy-oil or bitumen, while gas injection is widely used where sufficient gas supplies are available. Most importantly, oil recovery by carbon dioxide injection requires specific reservoir conditions to maintain the optimal sweep efficiency [2], particularly the to oil ratios in steam flooding for viscous oil in naturally fractured carbonate reservoirs, potentially opening the possibility of merging PSO-ANN with viscous oil recovery modeling software [20]. They demonstrated that PSO-ANN is superior to BP-ANN at forecasting in this type of oil field with the maximum error of less than 8%.…”

Section: Introductionmentioning

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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A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs

Cited by 105 publications

References 42 publications

Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs

Data Analytics Techniques for Performance Prediction of Steamflooding in Naturally Fractured Carbonate Reservoirs

Statistical Optimization for Production of Light Olefins in a Fluidized‐Bed Reactor

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

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