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Liu A.-H.,Gas Technology Institute | Liu A.-H.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development
Xiandai Huagong/Modern Chemical Industry | Year: 2015

Based on the water quality in Qinghua oil field, a barium sulfate scale inhibitor TH-60 is developed by using polyepoxy succinic acid (PESA), surfactants, additives and solvent. The result shows that TH-60 has scale inhibiting performance to barium, strontium and calcium ions, especially to barium sulphate. The scale inhibition rate to barium sulphate can reach 75% when the concentration of SO42- and Ba2+ and TH-60 are 1600 mg/l, 700 mg/l and 100 mg/l, respectively. ©, 2015, China National Chemical Information Center. All right reserved. Source


Ren D.,Northwest University, China | Sun W.,Northwest University, China | Zhao J.,Petrochina | Zhao J.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development | And 3 more authors.
Zhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology | Year: 2015

Due to the poor situation of microscopic waterflooding mechanism research on Chang 81 reservoir in Huaqing oilfield of Ordos basin, the authors quantitatively studied the influence factors of waterflooding characteristics and oil displacement efficiency by waterflooding seepage experiment with real sandstone micromodel, and test data of reservoir property, constant-speed mercury injection as well as nuclear magnetic resonance. The size and distribution of throat radius were also found closely related with the waterflooding seepage law. The results show that the microscopic seepage paths of Chang 81 reservoir include uniform displacement, mesh-uniform displacement, finger-mesh displacement and finger displacement, and their correspondent oil displacement efficiency reduces in turn under the same experimental conditions. More than 70% of residual oil flow around the grain or as an oil slick. Reservoir property, pore structure and saturation of movable fluid are controlled by diagenesis, which has consistent impact on waterflooding mechanism. Generally, when the permeability is greater than 1.5 mD, the throat radius is greater than 0.5 μm, the sorting coefficient is greater than 0.15, the saturation of movable fluid is greater than 40%, the displacement pressure increasing rate is greater than 50% and displacement velocity exceeds 0.012 mL/min, the increasing trend of oil displacement efficiency will be obviously weakened. This paper proposes that oil recovery data and the core waterflooding seepage experiment should be emphasized in the process of reservoir exploitation, and priority should be given to reservoir with high permeability in designing reasonable exploitation techniques and procedures. © 2015, China University of Mining and Technology. All right reserved. Source


Li X.,China National Offshore Oil Corporation | Li X.,China University of Petroleum - Beijing | Zhou J.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development | Zhou J.,Petrochina | And 4 more authors.
Petroleum Exploration and Development | Year: 2012

On the basis of the basic principles of optimization algorithms and classification algorithms, the Self-Organizing feature Map neural network (SOM) is applied to establish the predictive model of lithology for the K-Means optimized data set including core data, logging data and well tests data. Additionally, the decision tree and support vector machine are used to build the predictive model of fluid on the basis of the lithology identification. The optimization algorithms, including genetic, grid and quadratic, are adopted to optimize the important parameters of C-SVC and ν-SVC, such as C, ν and γ, so as to accurately identify the complex lithologies and multiphase fluids of complicated reservoirs. The SOM model and the decision tree and support vector machine are utilized to process four new wells in the complicated Carboniferous reservoirs of the Wucaiwan Sag, eastern Junggar Basin. The accuracy of lithology identification is 91.30%, and the accuracy of fluid identification is 95.65%. The lithologic complexity is not the main factor leading to the differences of fluids in the reservoirs. Because the complexity and nonlinearity of data set are not strong enough, the accuracy of the decision tree model is better than that of the support vector machine. Their accuracy rates are 94.31% and 86.97%, respectively. The performance of linear polynomial function is better than that of the radial basis function RBF and the neural function Sigmoid. The classification performance and generalization ability of C-SVC are stronger than that of the ν-SVC. © 2012 Research Institute of Petroleum Exploration & Development, PetroChina. Source


Li X.,China National Offshore Oil Corporation | Li X.,China University of Petroleum - Beijing | Zhou J.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development | Zhou J.,Petrochina | And 4 more authors.
Shiyou Kantan Yu Kaifa/Petroleum Exploration and Development | Year: 2012

On the basis of the basic principles of optimization algorithms and classification algorithms, the Self-Organizing feature Map neural network (SOM) is applied to establish the predictive model of lithology for the K-Means optimized data set including core data, logging data and well tests data. Additionally, the decision tree and support vector machine are used to build the predictive model of fluid on the basis of the lithology identification. The optimization algorithms, including genetic, grid and quadratic, are adopted to optimize the important parameters of C-SVC and υ-SVC, such as C, υ and γ, so as to accurately identify the complex lithologies and multiphase fluids of complicated reservoirs. The SOM model and the decision tree and support vector machine are utilized to process four new wells in the complicated Carboniferous reservoirs of the Wucaiwan Sag, eastern Junggar Basin. The accuracy of lithology identification is 91.30%, and the accuracy of fluid identification is 95.65%. The lithologic complexity is not the main factor leading to the differences of fluids in the reservoirs. Because the complexity and nonlinearity of data set are not strong enough, the accuracy of the decision tree model is better than that of the support vector machine. Their accuracy rates are 94.31% and 86.97%, respectively. The performance of linear polynomial function is better than that of the radial basis function RBF and the neural function Sigmoid. The classification performance and generalization ability of C-SVC are stronger than that of the υ-SVC. Source


Yang H.,Petrochina | Yang H.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development | Liu X.,Petrochina | Liu X.,National Engineering Laboratory for Low Permeability Petroleum Exploration and Development | Zhang D.,Petrochina
Natural Gas Industry | Year: 2013

Industrial gas flow was obtained in Shancan-1 and Yu-3 wells in the Ordos Basin in 1989 and the Jingbian Gas Field was discovered by then as the largest marine carbonate gas field in China, which confirms the huge potential of gas exploration in the lower Paleozoic marine carbonate reservoirs in this basin. In order to promote the early buildup of another Daqing in West China and provide robust technical support for further exploration in the Jingbian field, a summary was made of the main controlling factors of gas pooling in this study area as well as the involved technical approaches to gas exploration. Through the integrated analysis of depositional characteristics, source rock conditions, reservoir types and reservoir forming assemblages, three exploration domains in this Ordovician marine carbonate-developed basin were determined including the middle and eastern Ordovician weathering crust, dolomites in eastern Central paleouplifts, karst fractures and caverns in the west. After recent years' efforts in geological research and technological breakthrough, the main controlling factors of gas pooling in the above exploration domains have been well understood and a complete set of techniques have been formed such as a fine description of paleokarst landforms, forecast of weathering crust reservoirs, seismic prediction of dolomite reservoirs and detection of their gas-bearing property, recognization of fractures and caverns and reservoir prediction, and so on. Thus, new progress has been made in gas exploration in this study area. The discovered gas-bearing area in the Jingbian field has been increasing and gas reserves there nearly doubled, which is a realistic goal for the PetroChina Changqing Company to enhance its reserves and production. Multiple gas enrichment zones have been discovered in the dolomite reservoirs in the eastern Central paleouplift with natural gas in place of close to 100 billion m3, which is a realistic alternative domain. New signs of gas pooling have been found in the fractures and caverns in the western basin, which is a new area of further gas exploration. Source

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