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Collaborating Authors
Tianjin Branch of CNOOC, China Co., Ltd
Research and Application of Water Breakthrough Rules in Offshore Edge Water Heavy Oil Reservoirs
Chen, Cunliang (Tianjin Branch of CNOOC, China Co., Ltd) | Yue, Baolin (Tianjin Branch of CNOOC, China Co., Ltd) | Zhang, Junting (Tianjin Branch of CNOOC, China Co., Ltd) | Han, Xiaodong (Tianjin Branch of CNOOC, China Co., Ltd) | Wu, Xiaohui (Tianjin Branch of CNOOC, China Co., Ltd) | Wu, Xuanren (Dragon Stone Energy Ltd)
Abstract LD Oilfield is a heavy oil reservoir with edge water, which oil viscosity is 127~544mPaĀ·s. After 1 to 3 months of production, the water content of the oil well rose rapidly, and the oil production dropped rapidly. Therefore, it is very necessary to carry out research on the water breakthrough rules of edge-water reservoirs. Taking the offshore LD16 oil field as the research object, relevant research has been carried out using numerical simulation technology. First, a multi-layer model is established to study the change pattern of the law of water cut rise by setting different proportions of the reserves of each oil layer. Then a single-layer model was established to study the influence of factors on the water breakthrough time of oil wells, such as average permeability, grade difference, distance from edge water, water multiple, oil production speed, crude oil viscosity and other factors. And the relative range method was used to determine the main controlling factors. On this basis, the orthogonal design method is used to design the simulation plan. According to the simulation results, an empirical formula for predicting the water breakthrough time of edge water reservoirs is established using intelligent algorithms. Research shows that the difference in reserves between layers has an impact on the law of water cut rise. The water cut of oil wells rises more slowly when water cut in the main layer breaks, and the water cut of oil wells rises faster if water cut in the non-main layer breaks. The research results can be used to judge the water break. Compared with oil well profile test data, the agreement rate is as high as 93%. Distance from edge water, oil production speed, grade difference and crude oil viscosity are the main controlling factors that affect the water breakthrough time of LD16 oilfield. Intelligent algorithms can fit formulas well. The prediction result of the empirical formula is consistent with the actual situation of the oil field. Compared with the actual situation, the error rate is only 3.5%. The research results can be used to predict the water breakthrough time of oil wells. The research results have certain guiding significance for improving the development effect of heavy oil edge water reservoirs and will be extended to more oil fields.
- Asia > China (0.69)
- North America > United States > Texas > Harris County > Houston (0.28)
Research on the Law of Liquid Production Index of Bohai Typical Oilfield Based on Big Data
Yue, Baolin (Tianjin Branch of CNOOC, China Co., Ltd) | Liu, Bin (Tianjin Branch of CNOOC, China Co., Ltd) | Shi, Hongfu (Tianjin Branch of CNOOC, China Co., Ltd) | Shi, Fei (Tianjin Branch of CNOOC, China Co., Ltd) | Zhang, Wei (Tianjin Branch of CNOOC, China Co., Ltd)
Abstract The prediction of reservoir fluid production law play a key role in offshore oil field development plan design. It determines the parameter selection of pump displacement, oilfield submarine pipe capacity, platform fluid handling capacity, power generation equipment, etc. If the liquid production forecast is too low, the capacity will be expanded later, while if the forecast is too high, it will result in a waste of investment, which directly affects the fixed investment in oilfield development. Based on the statistical analysis of big data, this paper applies the dynamic data of all single wells and full life cycle of the oil field to analyze the dimensionless liquid production index (DLPI) law, and further establish the liquid production index prediction formula on this basis. Thus, the different types of Bohai plate and statistical table of the characteristics of the DLPI of the reservoir are completed. The results show that the DLPI of Bohai Sea heavy oil reservoir are following: water cut < 60 % indicates the trend is flat; water cut between 60 ā¼ 80 % illustrates the slow growth (water cut 80 % is 2.5ā¼3 times); water cut > 80 % shows rapid growth (water cut 95% is 5.5ā¼6 times). The DLPI of Bohai Sea conventional oil reservoir are as following: when the water cut < 60%, the DLPI drops first, and then increase when the water cut is about 30% (the lowest point (0.7ā¼0.9 times)). When the water cut rise to 60%, the DLPI returns to 1 times; When the water cut is 60ā¼80%, it grows slowly (1.5ā¼2 times); when the water cut > 80 %, it grows rapidly (water cut 95% is 2ā¼3 times). The study may provide a guidance to the prediction of the amount of fluid in offshore oilfields, provide a basis for the design of new oilfield development schemes and increasing the production of old oilfields.
- Asia > China (0.47)
- North America > United States > Texas (0.34)
- Research Report > New Finding (0.35)
- Research Report > Experimental Study (0.35)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.66)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.64)
The Model of Sandbody Controlled by Dynamic Provenance System and its Exploration Significance in Superposition Area of Strike-Slip and Extension Stress in the South of Bohai Sea
Zhen, Huang (Tianjin Branch of CNOOC, China Co., Ltd) | Bo, Yang (Tianjin Branch of CNOOC, China Co., Ltd) | Guoying, Li (Tianjin Branch of CNOOC, China Co., Ltd) | Jian, Ren (Tianjin Branch of CNOOC, China Co., Ltd) | Xiaoling, Wang (Tianjin Branch of CNOOC, China Co., Ltd)
Abstract Laizhouwan sag in Bohai Bay basin is a fault basin controlled by extensional fault depression and strike slip pull apart, which is an important oil and gas exploration area in Bohai Bay. Exploration practice shows that the prediction of high quality reservoir is the core problem of exploration in this area. Based on the analysis of drilling, seismic data and structural physical simulation in Laizhouwan depression, this paper analyzes the structural deformation under the stress field of strike slip extensional superposition, and points out the dynamic source controlled sand model in the strike slip extensional superposition area. Firstly, The structural response of "pressure relief settlement, pressure boosting uplift" under the mechanism of strike slip extension stress superposition stress is the root cause of block uplift drop alternation transformation. As a result, the southern slope zone of Laizhouwan depression shows the structural pattern of early uplift and late uplift in the East and early uplift and late uplift in the west, forming a "seesaw" structural evolution pattern. Secondly, the unique paleogeomorphology controls the orderly distribution of sedimentary system in time and space. In the Paleocene, the east uplifted, forming a local provenance system. In the denudation area above the slope break developed fracture weathering shell type reservoirs, and the subsidence area under the slope break developed fan delta deposits; In the early Eocene, the relatively flat platform palaeogeomorphology was developed, which created favorable conditions for the development of mixed sedimentary body of lacustrine carbonate and delta; At the end of Eocene, the West was pressurized and uplifted, the East was released and subsided, and the braided river delta sediments of Western provenance were developed. Under the guidance of this recognition, the hidden dynamic provenance was successfully identified in the study area.
- Geology > Structural Geology > Fault (1.00)
- Geology > Structural Geology > Tectonics (0.94)
- Geology > Sedimentary Geology > Depositional Environment > Transitional Environment > Deltaic Environment (0.56)
A New Method to Quantify Interwell Connectivity Using Performance Data and Intelligence Algorithm
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.
- Asia (0.95)
- North America > United States > Texas (0.47)
Water Flooding Performance Prediction in Layered Reservoir Using Big Data and Artificial Intelligence Algorithms
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.
A New Model to Infer Interwell Connectivity in Low Permeability Oil Field
Wang, Xiang (Changzhou University) | Chen, Cunliang (Tianjin Branch of CNOOC, China Co., Ltd) | Han, Xiaodong (Tianjin Branch of CNOOC, China Co., Ltd) | He, Yanfeng (Changzhou University) | Liu, Xue (China zhenhua Oil Co., Ltd) | Lv, Qichao (China University of Petroleum) | Dong, Peng (China University of Petroleum)
Abstract This paper presents a new model to infer interwell connectivity in low permeability oil field. Establishing connectivity among various injectors and producers is a key to improve the understanding of a reservoir under waterflood. This understanding improves the estimates for ultimate recovery and also helps to better define the future development plan. Because of the complex pore structure, the seepage law of low permeability oilfield is more complex. Therefore, it is difficult to study the interwell connectivity. There is a threshold pressure gradient in fluid flow in low permeability reservoirs. Therefore, the productivity formula of low permeability reservoir is deduced from the equation of motion, which is a linear model when single phase flow or water cut is stable. Similar to the capacitanceāresistance model (CRM), the productivity formula and the material balance equation are combined to get the calculation model of accumulative liquid production and cumulative water injection. And the connection coefficient is solved by using the least square principle and genetic algorithm. Different numerical simulation cases are employed to validate the derived method. The traditional method is the relationship between the instantaneous liquid production and the instantaneous water injection, and this model is the relationship between the cumulative liquid production and the cumulative water injection. In addition, the paper presents a case study to compare findings from the results of the new model and the use of interwell tracer. The results obtained from the new model shows good agreement with the results obtained from interwell tracer. The new model has been applied to nearly many wells. 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. This method is a rapid way to have a more reliable understanding of the reservoir heterogeneity and quick prediction of reservoir performance to optimize the waterflood. The method was applied to an oil field which is effective for establishing well interaction pattern. Recommendations were given to improve waterflood efficiency.
- Asia > China (0.96)
- North America > United States > Texas (0.69)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 30/2 > J-Block Field (0.99)
- Asia > China > Shanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Shaanxi > Ordos Basin > Changqing Field (0.99)
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