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Chen, Cunliang (Tianjin Branch of CNOOC, China Co., Ltd) | Yang, Ming (Tianjin Branch of CNOOC, China Co., Ltd) | Han, Xiaodong (Tianjin Branch of CNOOC, China Co., Ltd) | Zhang, Jianbo (China University of Petroleum, East China)
Abstract Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods. This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance. This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy. By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.
Chen, Cunliang (Tianjin Branch of CNOOC China Co., Ltd) | Han, Xiaodong (CNOOC Ltd and China University of Petroleum, Beijing) | Zhang, Wei (Tianjin Branch of CNOOC China Co., Ltd) | Zhang, Yanhui (Tianjin Branch of CNOOC China Co., Ltd) | Zhou, Fengjun (Tianjin Branch of CNOOC China Co., Ltd)
Abstract The ultimate goal of oilfield development is to maximize the investment benefits. The reservoir performance prediction is directly related to oilfield investment and management. The traditional strategy based on numerical simulation has been widely used with the disadvantages of long run time and much information needed. It is necessary to form a fast and convenient method for the oil production prediction, especially for layered reservoir. A new method is proposed to predict the development indexes of multi-layer reservoirs based on the injection-production data. The new method maintains the objectivity of the data and demonstrates the superiority of the intelligent algorithm. The layered reservoir is regarded as a series of single layer reservoirs on the vertical direction. Considering the starting pressure gradient of non-Newtonian fluid flow and the variation of water content in the oil production index, the injection-production response model for single-layer reservoirs is established. Based on that, a composite model for the multi-layer reservoir is established. For model solution, particle swarm optimization is applied for optimization of the new model. A heterogeneous multi-layer model was established for validation of the new method. The results obtained from the new proposed model are in consistent with the numerical simulation results. It saves a lot of computing time with the incorporation of the artificial intelligence methods. It showed that this technique is valid and effective to predict oil performance in layered reservoir. These examples showed that the application of big data and artificial intelligence method is of great significance, which not only shortens the working time, but also obtains relatively higher accuracy. Based on the objective data of the oil field and the artificial intelligence algorithm, the prediction of oil field development data can be realized. This technique has been used in nearly 100 wells of Bohai oilfields. The results showed in this paper reveals that it is possible to estimate the production performance of the water flooding reservoirs.
Libing, Fu (Research Institute of Petroleum Exploration & Development, CNPC) | Jun, Ni (Research Institute of Petroleum Exploration & Development, CNPC) | Zifei, Fan (Research Institute of Petroleum Exploration & Development, CNPC) | Xuanran, Li (Research Institute of Petroleum Exploration & Development, CNPC)
Abstract Oilfield development generally goes through multiple stages of development. Different stages have different development effects and features. Waterflooding characteristic curve is one of the main methods of development evaluation and oil recovery forecast in waterflooding oilfields. The chart of relationship between water content and recovery degree is directly applied to predict recovery factor. However, the production data could not be in accordance with the curve in the chart in the course of practical application. In order to improve the precision and applicability of water drive characteristic curve charts, based on seepage mechanics and material conservation principle, waterflooding characteristic formula between accumulative oil production and accumulative liquid production was deduced. Using mathematical equation method, a new relation between water-cut and recovery degree considering different time-phased actual development data was presented and the corresponding formula of water cut increasing rate was derived. Fitting parameters by regression fitting method from the actual production data was employed as the reference value for the revised mathematical model and the corresponding water drive curve cluster was drawn based on the fitting-curve. At the same time, the method of time-phased water drive effect evaluation was presented. Results showed that recovery factor of waterflooding reservoirs predicted by improved chart is more exact and the fitting degree of water cut between calculated and actual data is better than by existing charts, demonstrating that the improved chart has strong practicability in waterflooding development effect evaluation and index forecast. Those conclusions improve the waterflooding theory and provide useful guidance for effect evaluation of water displacing, water cut calculation and oil recovery ratio prediction for different stages. The presented research content furthers the theory of waterflooding, and builds theoretical foundation for the technologies of development evaluation in waterflooding reservoirs.
Liu, Xue (China zhenhua Oil Co., Ltd) | Qu, Xiangyun (China zhenhua Oil Co., Ltd) | Jiang, Ming (China zhenhua Oil Co., Ltd) | Huang, Jing (CNOOC Research Institute) | Chen, Cunliang (Tianjin Branch of CNOOC, China Co., Ltd)
Interwell dynamic connectivity is one of the important indicators for development evaluation of water flooding oilfields. It is widely used in identification of dominant channels, judgement of fracture sealing and evaluation of development effect. The traditional capacitance-resistance model (CRM) can not consider the change of start-up pressure gradient and liquid production index of heavy oil reservoir. For better quantifying the interwell connectivity of heavy oil reservoir, a new method is proposed using performance data and intelligence algorithm.
Experiments show that there is a starting pressure gradient in heavy oil flow. The pseudo-start pressure gradient model is used to characterize this phenomenon. On this basis, the fluid production model of heavy oil reservoir is deduced and established. The results show that the liquid yield index increases with the increase of water content, which is not a constant. And then, a time constant function is constructed to characterize the lag and attenuation of water flooding signals in formation propagation. By substituting the production model and the time constant function into the equation of material balance, a new model is established by difference method. The solution of the new model is transformed into an optimization problem by using the least squares principle. And the quantitative connectivity evaluation is obtained by using frog leaping algorithm.
The new model has been applied in many oilfields. Compared with the single-well dynamic analysis, the accuracy of the new model is very high, but the spent time is less than half of the single-well dynamic analysis. In addition, the paper presents a case study to compare findings from the results of the new model and the use of interwell tracer and interference well testing. The results obtained from this paper shows good agreement with the results obtained from interwell tracer or interference well testing. However, compared with the tracer test or interference well testing, the new method can save a lot of money. In summary, this paper is not only feasible, but also saves a lot of time and money.
The new method considers not only the starting pressure gradient of heavy oil, but also the change of liquid production index. It is an effective method for evaluating injection-production connectivity.
Liu, Yigang (Tianjin Branch of CNOOC Ltd) | Han, Xiaodong (CNOOC Ltd and China University of Petroleum, Beijing) | Chen, Cunliang (Tianjin Branch of CNOOC Ltd) | Wang, Hongyu (Tianjin Branch of CNOOC Ltd) | Liu, Hao (Tianjin Branch of CNOOC Ltd)
Prediction of oilfield development data is one of the important technical indicators for oilfields to achieve maximum economic benefits. Numerical simulation method is the most commonly used method. However it has been widely used with a long run time and too much information to solve this problem. With the continuous development of big data technology and artificial intelligence technology, there is an urgent need to form a development index prediction method based on such technologies.
This paper proposed a new method to predict water flooding performance in layered reservoir. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively, which considers invasion of natural water and the start-up pressure gradient of heavy oil reservoir. And then a composite model is established only by production data. Finally, the least square principle and artificial intelligence algorithm are used to optimize the model. Then the Gentil's correlation method is used to predict production performance. As development progresses, the production data became more and more abundant, and they replaced the model and re-optimized it. Oilfield applications showed that this technique has capability to predict oil performance in layered reservoir.