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Liu, Yingxian (Tianjin Branch of CNOOC China Co., Ltd) | Ma, Kuiqian (Tianjin Branch of CNOOC China Co., Ltd) | Cai, Hui (Tianjin Branch of CNOOC China Co., Ltd) | Chen, Cunliang (Tianjin Branch of CNOOC China Co., Ltd) | Wang, Xiang (Changzhou University) | Dong, Peng (China University of Petroleum)
Abstract Interwell connectivity is an important parameter for reservoir characterization and is essential for making decisions on optimization of water injection and infill wells. Unlike ordinary reservoir, starting pressure gradient is an important characteristic for heavy oil reservoir. For better quantifying the interwell connectivity of heavy oil reservoir, a robust method to quantify interwell connectivity using performance data is proposed. Considering the characteristics of the starting pressure of heavy oil flow, the production model between the yield and the pressure difference is established. And the production model is linear when the steady flow and the water content are relatively stable. And then, a time constant function is constructed to characterize the lag and attenuation of waterflood signals in formation propagation. By substituting the production model and the time constant function into the equation of material balance, the relationship model between cumulative liquid production and the cumulative water injection (CPIM) is obtained through two integrals. The cumulative relation overcomes the instability of instantaneous relations. The solution of the CPIM is transformed into an optimization problem by using the least squares principle. And the quantitative connectivity evaluation is obtained by using PSO algorithm. Compared with the single-well dynamic analysis, the accuracy of the CPIM is over 90%, 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 CPIM and the use of interwell tracer. The results obtained from the CPIM shows good agreement with the results obtained from interwell tracer. And the CPIM can be applied to some wells which can not be carried out tracer test because geological conditions of these wells are particularly complex. In summary, this CPIM is not only feasible, but also saves a lot of time and money. The CPIM can be used to quickly estimate reservoir properties and infer interwell communication in primary and secondary recovery from available data with high confidence. The method was applied to Bohai oil field which is effective for establishing well interaction pattern. Recommendations were given to improve waterflood efficiency.
This paper presents the use of hydraulic interwell connectivity concepts to characterize the reservoir for waterflood performance evaluation. These hydraulic interwell connectivity concepts are presented in terms of two indices: The Interwell Flow Capacity Index (IFCI) and the Hydraulic Interwell Connectivity Index (HICI). This approach utilizes the reservoir flow capacity and production/injection performance data to calculate the IFCI and HICI. The spatial distribution and correlations of these indices are useful to evaluate the reservoir characteristics for waterflood design and performance analysis. A Colombian (South America) sandstone reservoir in La Cira Field is used to illustrate the application of these concepts.
Abstract Reservoir rock wettability is an important parameter to consider for oil recovery optimization. The great majority of sandstone formations is known to be strongly water-wet. In contrast, most carbonate reservoir rocks are believed to be mixed-wet or oil-wet to some degree with a non-uniform distribution of the wettability in the reservoir. Despite the importance of this parameter there is currently no proven quantitative logging technique that can provide a continuous wettability log. A detailed analysis of a new model for the conductivity of reservoir rock called the ‘connectivity equation’ is provided in the paper. Similar to Archie's law this simple model has only two parameters: An exponent called the conductivity exponent µ, and the water connectivity parameter Cw. Under some conditions Cw can be equal to zero and the equation becomes identical to Archie's law in its simplest form (n = m = µ). However, in the general case the model is fundamentally different from Archie's law because in the connectivity equation resistivity is only a function of the water volume fraction. Cw is shown to account for water connectivity effects in the pore network. These effects are encoded in the expression of Cw with three terms linked respectively to 1-the ‘super-connectivity’ of micro pores in micritic grains for carbonate rocks, or the super-connectivity created by shale in shaly sandstones, 2- wettability effects in meso/macro pores, and 3-the low connectivity of vuggy porosity. This model is compared with published data and is shown to correctly account for most situations, including 'non-Archie' rocks such as low resistivity pay in carbonates, strongly oil-wet rocks, and the dual water model for shaly sands. A good correlation between Cw - obtained from a combination of wireline logs - and wettability measured on cores is found on data from a Middle East carbonate reservoir.
He, Youwei (China University of Petroleum, Beijing) | Cheng, Shiqing (China University of Petroleum, Beijing) | Li, Lei (China University of Petroleum, Beijing) | Mu, Guoquan (Research Institute of Petroleum Exploration and Development, Changqing Oilfield) | Zhang, Tiantian (University of Texas at Austin) | Xu, Hainan (China University of Petroleum, Beijing) | Qin, Jiazheng (China University of Petroleum, Beijing) | Yu, Haiyang (China University of Petroleum, Beijing)
Abstract Due to the effect of reservoir heterogeneity and micro-fractures in low permeability reservoirs, effectively characterization of waterflood direction and front has become a tough issue under high water-cut condition. In order to achieve better understanding of such a complex problem, a workflow, containing statistical and numerical techniques, is developed to characterize waterflood direction and front distribution in Changqing Oilfield by employing both flow rates and bottom-hole pressure (BHP) data. The workflow includes four steps: first, dynamic analysis is used to qualitatively investigate the relationships between injector and producers. Then, constraint multiple linear regressions (MLR) method is applied to calculate the interwell connectivity coefficients, which was used to quantitatively describe the waterflood direction by injection and production rates. Based on the results of the two former steps, we can adopt numerical well testing as our third step to deal with flow rates and BHP data to characterize the waterflood direction and front. Finally, streamline method is employed to simulate the waterflood front and high-permeability channels distribution based on the outcome of the three preceding techniques. We apply this workflow to the well group W16 in Changqing Oilfield, and the results show that we can acquire better understanding of waterflood performance under high water-cut condition, including interwell connectivity, waterflood direction, and waterflood front distribution. Compared to individual method, the proposed workflow can offer more perspectives and ways to make a comprehensive and deep investigation of the waterflood reservoir. With the help of the information obtained by this workflow, operators could make more reasonable decisions on waterflood management such as well pattern optimization and injection-production parameters adjustment.
Yong, Li (Research Institute of Petroleum Exploration and Development PetroChina) | Baozhu, Li (Research Institute of Petroleum Exploration and Development PetroChina) | Jiasheng, Zhou (China National Oil and Gas Exploration and Development Corporation, PetroChina)
Abstract M1 reservoir is a large multi-layered sandstone reservoir in Middle East, which is under primary depletion and edge aquifer drive. There are lots of sources of water production data in M1, and water production data are one of the most important and invaluable surveillance data to understand reservoir connectivity. This paper proposes a method to show how to integrate all sources of aquifer influx surveillance data to evaluate reservoir connectivity of M1. There are four types of aquifer influx identification data in M1 reservoir, and different type of surveillance data are analyzed in detail. Through aquifer influx analysis, it can be confirmed if wells are aquifer flooded in some zones. Then, combined geological understanding with well aquifer breakthrough time and well water cut change characteristic analysis, the possible aquifer influx zone is determined. Finally, aquifer support and sand body connectivity around water flooded wells are better understood, which is helpful and useful for next waterflooding development. M1 reservoir is a large multi-layered sandstone reservoir of deltaic environment with oil bearing area around 500Km2 in Middle East. And M1 is influenced by fluvial, tide and wave, which results in great variations of sand bodies' distribution, reservoir quality and connectivity. Furthermore, M1 reservoir is still under primary depletion with reservoir pressure close to saturation pressure, so waterflooding should be applied urgently. Four types of data were analyzed to study the aquifer influx, which including measured water cut data, flowtest data, saturation logging data and SGS data. Through data analysis, the confirmed aquifer-influx wells and possible aquifer -influx wells are determined, and water breakthrough time and water cut change characteristic are also determined. And based on the characteristic, four areas with different characteristic are classified. Combined with the geological understanding, it is found that the connectivity within each area are similar, but there are barriers among different areas which results in poor communication among different areas. Also the water breakthrough zone of each area are different, and it is useful to understanding aquifer support and reservoir lateral heterogeneity of different zones. Furthermore, aquifer influx has preferred direction, which mainly moves along with the channels axis. This phenomenon should be considered in well pattern decision making during the following waterflooding study. This paper offers a case study of reservoir connectivity analysis based on different types of aquifer influx surveillance data analysis. And the understanding is also much valuable and useful for depositional facies mapping and the next waterflooding well pattern selection and decision. It also provide a reference for the related study on other similar field.