Development of a fuzzy saturation index for sulfate scale prediction

Khatami, H. R.; Ranjbar, Mohammad; Schaffie, Mahin; Emady, M.A.

doi:10.1016/j.petrol.2009.12.006

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…Moreover, deposition of sulfate scales is mainly caused by the injection of seawater saturated with sulfate anion into a formation containing high calcium, strontium, and barium cations for water flooding (Crabtree et al 1999;Frenier and Ziauddin 2008;Khatami et al 2010;McElhiney 2001;Collins 2005). As for water incompatibility, barium sulfate scales are also formed with changes in the pressure, temperature, and concentration of relevant ions (BinMerdhah et al 2010;Crabtree et al 1999;Frenier and Ziauddin 2008;Liu et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based models for predicting permeability impairment due to scale deposition

Ahmadi

Chen

2020

J Petrol Explor Prod Technol

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Water injection is one of the robust techniques to maintain the reservoir pressure and produce trapped oil from oil reservoirs and improve an oil recovery factor. However, incompatibility between injected water and reservoir water causes an unflavored issue named "scale deposition." Owing to the deposited scales, effective permeability of a reservoir reduced, and pore throats might be plugged. To determine formation damage owing to scale deposition during a water injection process, two well-known machine learning methods, least squares support vector machine (LSSVM) and artificial neural network (ANN), are employed in the present paper. To improve the performance of the LSSVM method, a metaheuristic optimization algorithm, genetic algorithm (GA), is used. The constructed LSSVM model is examined using real formation damage data samples experimentally measured, which was reported in the literature. According to the obtained outputs of the above models, LSSVM has a high performance based on the correlation coefficient, and infinitesimal uncertainty based on a relative error between the model predictions and the corresponding actual data samples was less than 15%. Outcomes from this study indicate the useful application of the LSSVM approach in the prediction of permeability reduction due to scale deposition, and it can lead to a better and more reliable understanding of formation damage effects through water flooding without expensive laboratory measurements.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based models for predicting permeability impairment due to scale deposition

Ahmadi

Chen

2020

J Petrol Explor Prod Technol

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“…Upon mixing these two incompatible waters, sulphate scales (i.e. BaSO 4 , SrSO 4 , and CaSO 4 ) may form which cause pore blockage once deposited . Formation of BaSO 4 and its deposition increases by a decrease in pressure and temperature as well as an increase in barium and sulphate ion concentrations …”

Section: Introductionmentioning

confidence: 99%

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

2017

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One of the detrimental issues in upstream oil industry is permeability impairment in oil reservoirs. The permeability may be reduced because of scale deposition originated from injected water incompatibility with the in‐situ water. To tackle this issue and monitor permeability impairment owing to scale deposition, an inventive evolutionary approach of Artificial Neural Network (ANN) is utilized which is based on the bio‐inspired science. This approach is optimized by various optimization algorithms which provide a high‐precision decision‐making process with low uncertainty associated with the interconnected weights of the developed neural network model. The constructed intelligent approaches are evaluated using extensive experimental data from the open literature. Moreover, two regression models are developed to highlight the robustness and precision of the addressed techniques. For model validation, the predictions obtained from the smart technique as well as the regression method are compared with the formation damage experimental data. Based on various performance criteria, it was obtained that the predictions from the optimized genetic algorithm have infinitesimal average relative and absolute deviation from the experimental data (< 0.1 %). The results obtained in the current research prove that implication of HGAPSO‐ANN in monitoring of formation damage associated with scale deposition in porous media results in reliable estimation of permeability impairment. This can result in designing a more reliable reservoir simulation model that captures the permeability impairment due to scale deposition, hence providing thorough action plans for developing EOR technologies that may cause scale deposition‐related formation damage.

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“…Since seawater contains high concentration of sulfate anions and formation water contains high concentration of calcium, strontium and barium cations, mixing them causes formation of sulfate scales such as calcium sulfate, strontium sulfate and barium sulfate [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Gamma transmission system for detection of scale in oil exploration pipelines

Oliveira

Nascimento

Marinho

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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a b s t r a c tThe goal of this study is to detect and quantify the thickness of the scale layer inside the service pipelines. For this purpose, a system composed of a 137 Cs source and a NaI(Tl) scintillation detector was developed. The source and the detector were positioned in geometry to make transmission measurements. A computational routine was developed to generate simulated data, which were later compared with the experimental ones, in order to calculate the thickness of scale layer. The results of the validation tests show similar values to the real ones. It was therefore possible to conclude that the procedure is efficient for the detection and quantification of materials inside pipelines, and that the system is advantageous for its application in the field, especially with respect to radioprotection.

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Development of a fuzzy saturation index for sulfate scale prediction

Cited by 17 publications

References 5 publications

Machine learning-based models for predicting permeability impairment due to scale deposition

Machine learning-based models for predicting permeability impairment due to scale deposition

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

Gamma transmission system for detection of scale in oil exploration pipelines

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