A New Laboratory Method for Evaluation of Sulfate Scaling Parameters from Pressure Measurements

Carageorgos, Themis; Marotti, Marcelle; Bedrikovetsky, Pavel

doi:10.2118/112500-pa

Cited by 14 publications

(3 citation statements)

References 41 publications

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“…Yassin (2007, 2009) reported that increasing temperature, pressure head, and brine concentration had an unfavorable impact on the permeability reduction and reaction rate constant. Clarifying the constant for each model of scale deposition, more accurate for artificial cores than for natural reservoir cores based on a series of core flooding tests dependent on pressure measurements, was done by Carageorgos et al (2010). Figuring the interaction between reservoir lithology and a scale inhibitor has recently turned into researchers' interest like Todd (2012), who did some tests within modern phosphorus inhibitors that have some advantages in comparison with the conventional types.…”

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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“…These results were confirmed by a similar following study performed by Merdhah and Yassin using Berea sandstone cores. Utilization of pressure measurements through a novel methodology was reported by Carageorgos et al to determine scale deposition coefficients in coreflooding experiments. The proposed calculation methodology was found to be more precise for the artificial cores, compared to the natural reservoir cores.…”

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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“…Amount of the parameter is the same in both of the Moghadasi et al (2004) experiments. This is because of the same pore space properties in these experiments (Carageorgos et al 2010). The low velocity in the first experiment of Moghadasi et al (2004) performs bridging of particles that results in the higher rate of the particles capturing ( 0 ) in (16) Fig.…”

Section: Dbf Model Verification With Experimental Datamentioning

confidence: 88%

Data-driven approach for evaluation of formation damage during the injection process

Shabani

Jahangiri

Shahrabadi³

2019

J Petrol Explor Prod Technol

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Waterflooding is among the most common oil recovery methods which is implemented in the most of oil-producing countries. The goal of a waterflooding operation is pushing the low-pressure remained oil of reservoir toward the producer wells to enhance the oil recovery factor. One of the important objects of a waterflooding operation management is understanding the quality of connection between the injectors and the producers of the reservoir. Capacitance resistance model (CRM) is a data-driven method which can estimate the production rate of each producer and the connectivity factor between each pair of wells, by history matching of the injection and production data. The estimated connectivity factor can be used for understanding the quality of connection between the wells. In the waterflooding operation, the injected water always has the potential of causing formation damage by invasion of foreign particles deep bed filtration (DBF), mobilization of indigenous particles (fines migration), scale formation, etc. The formation damage can weaken the quality of connection (connectivity factor), between the injectors and producers of the field, increasing the skin of injection well. In this paper, DBF is used for creation of formation damage in synthetic reservoir models. Then, it has been tried to find the existence and amount of formation damage by evaluating the connectivity factor of CRM. Finally, the results of that have been used for prediction of skin variation in a real case by using the connectivity factor of CRM.

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A New Laboratory Method for Evaluation of Sulfate Scaling Parameters from Pressure Measurements

Cited by 14 publications

References 41 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

Data-driven approach for evaluation of formation damage during the injection process

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