15,329 research outputs found

    Data Analysis and Neuro-Fuzzy Technique for EOR Screening : Application in Angolan Oilfields

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    This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscriptPeer reviewedPublisher PD

    Fractional Flow Analysis for Chemical Flooding in Enhanced Oil Recovery

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    The context of this project is focused on analyzing how fractional flow governs efficiency in enhanced oil recovery and behavior of a reservoir upon chemical flooding. The study of the project is pursued mainly in the sense of manipulation of capillary number; mobility ratio and conformance which is further extrapolated through the calculation of target oil, rate and capillary number, surfactant retention, oil recovery algorithms and production functions. End results of this project are presented with graphical user interface, GUI that provides an efficient screening method of reservoir potentiality and recovery efficiency. Finally, the project is concluded with a detailed list of analysis summary which includes reservoir recovery efficiency as well as cumulative gas, oil and water produced from the reservoir

    Screening reservoir candidates for enhanced oil recovery (EOR) in Angolan offshore projects.

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    The neuro-fuzzy (NF) approach presented in this work is based on five (5) layered feedforward backpropagation algorithm applied for technical screening of enhanced oil recovery (EOR) methods. Associated reservoir rock-fluid oilfield data from successful EOR projects were used as input and predicted output in the training and validation processes, respectively. The developed model was then tested by using data set from Block B of an Angolan oilfield. The results of the sensitivity analysis between the Mamdani and the Takagi-Sugeno-Kang (TSK) approach incorporated in the algorithm has shown the robustness of the TSK ANFIS (Adaptive Neuro-Fuzzy Inference System) approach in comparison to the other approach for the prediction of a suitable EOR technique. The simulation test results showed that the model presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block B, Angola) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques for EOR

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

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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 steam flooding in NFCRs..

    Development and testing of advanced methods for the screening of Enhanced-Oil-Recovery techniques

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    Enhanced Oil Recovery (EOR) techniques must undergo preliminary laboratory and pilot testing before implementation to field-wide scale, and the whole evaluation process requires heavy investments. Hence forecasting EOR potential is a key decision-making element. A critical difference amongst EOR techniques resides in the oil-displacement mechanism upon which they are based. The effectiveness of these mechanisms depends on oil and reservoir properties. As such, similar EOR techniques are typically successful in fields sharing similar features. Here we implement and test a screening method aimed at estimating the optimal EOR technique for a target reservoir. Our approach relies on the information content tied to an exhaustive set of EOR field experiences. The basic screening criterion is the analogy with known reservoir settings in terms of oil and formation properties. Analogy is assessed by grouping fields into clusters: we rely on a Bayesian hierarchical clustering algorithm, whose main advantage is that the number of clusters is not set a priori but stems from data statistics. As a test bed, we perform a blind test of our screening approach by considering 2 fields operated by eni. Our predictions for analogy assessment are in agreement with the EOR techniques applied or planned in these fields

    Screening guidelines and data analysis for the application of in-situ polymer gels for injection well conformance improvement

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    Excessive water production represents a major industry challenge because of its serious economic and environmental impacts. Polymer gels have been effectively applied to mitigate water production and extend the productive lives of mature oilfields. However, selecting a proper gel technology for a given reservoir is a challenging task for reservoir engineers because of the associated geological and technical complexities and the absence of efficient screening tools. A comprehensive review for the worldwide gel field projects was conducted to develop an integrated systematic methodology that determines the applicability of three injection well gel technologies including bulk gels, colloidal dispersion gels, and weak gels. Comparative analysis, statistical methods, and a machine learning technique were utilized to develop a conformance agent selection advisor that consists of a standardized selection system, conventional screening criteria, and advanced screening models. The results indicated that gel technology selection is a two-step process that starts by matching problem characteristics with gel technical specifications and mechanisms. Then, the initial candidate technology is confirmed by screening criteria to ensure gel compatibility with reservoir conditions. The most influential conformance problem characteristics in the matching process are channeling strength, volume of problem zone, problem development status, and the existence of crossflow. In addition to crossflow, the presence of high oil saturations or unswept regions in the offending zones requires the application of flood-size treating technologies that combine both displacement and diversion mechanisms. The selection and design of gel technologies for a given conformance problem greatly depend on the timing of the gel treatment in the flood life --Abstract, page iv

    Application of artificial intelligence for technical screening of enhanced oil recovery methods

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    Acknowledgment This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use data and publish this manuscript.Peer reviewedPublisher PD

    Extraction and Fractionation of Pigments from Saccharina latissima (Linnaeus, 2006) Using an Ionic Liquid plus Oil plus Water System

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    There is a strong industrial interest in the development of greener and more sustainable processes based on the use of renewable resources, and a biorefinery based on marine resources, such as macroalgae, stands as a major opportunity toward that end. In this work, Saccharina latissima (Linnaeus), a brown macroalga, was used as a source of pigments to develop an integrated platform that is able to promote the extraction and separation of chlorophyll and fucoxanthin in one single step. The process was studied, and its operational conditions were optimized with yields of extraction of chlorophyll and fucoxanthin of 4.93 ± 0.22 mgchl·gdry biomass–1 and 1956 ± 84 μgfuco·gdry biomass–1, respectively. These results were achieved with extraction systems composed of 84% of an aqueous solution of a tensioactive phosphonium-based ionic liquid (IL) at 350 mM + 16% of sunflower oil, during 40 min, using a solid–liquid ratio of 0.017 gdry biomass·mLsolvent–1. After the separation of both aqueous IL-rich and oil-rich phases, the IL content in both phases was investigated, the oil phase being free of IL. Envisioning the industrial potential of the process developed in this work, the recovery of the IL from the aqueous IL-rich phase of the initial system was attempted by a back-extraction using organic solvents immiscible in water, being shown that 82% of the IL can be recovered and reused in new cycles of extraction. The environmental and economic impacts of the final process proposed for the extraction and fractionation of chlorophyll and fucoxanthin were evaluated. Different scenarios were considered, but summing up the main results, the solvents’ recycling allowed better results, proving the economic and environmental viability of the overall process
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