Liu, Guoqiang (PetroChina Exploration and Production Company) | Hou, Yuting (PetroChina Changqing Oilfield Company) | He, Junling (PetroChina Jilin Oilfield Company) | Zhang, Hao (PetroChina Xinjiang Oilfield Company) | Wu, Jinlong (Schlumberger) | Zhao, Xianran (Schlumberger) | Li, Huayang (Schlumberger) | Wu, Fangfang (Schlumberger) | Li, Shenzhuan (Schlumberger) | Wang, Yuxi (Schlumberger)
Most shale oil resources in China are lacustrine deposit. The reservoirs are usually characterized by complex lithology and high heterogeneity with various mineral compositions (quartz, carbonates, feldspars, pyrites and volcanic ash), total organic carbon and pore structure. How to delineate the shale oil reservoir, how to identify the ‘sweet spots’ and its distribution are the two major challenges and objectives for this study.
To answer the question, a systematic workflow was proposed by integrating the advanced logging technologies (such as nuclear magnetic resonance, micro-resistivity imager, spectroscopy data, array dielectric tool) with special core measurement data. Firstly, the shale oil reservoir was classified into different types according to the logging responses. Secondly, core samples were chosen from each type and sent out to lab for a series of core special experiments to test the microscopic properties. Finally, the advanced core analysis results and logging technologies were integrated to depict the characters of the different types of shale oil reservoirs from microscopic to macroscopic scale. And by comparing with testing data, the features of best shale oil reservoir type can be identified, and the distribution and potential of shale oil reservoir can be unraveled.
The new approach helped to get a thorough understanding of the shale oil reservoirs characteristics, such as lithology, mineral composition, pore types, pore size distribution, oil content, kerogen type and maturity of organic matter, organic carbon content and distribution. Six different kinds of shale oil reservoir facies were classfied from loging responses, inculding super high gamma ray siliceous shale, high gamma ray siliceous shale, high gamma ray argillaceous shale, high gamma ray tuffaceous shale, medium gamma ray siliceous shale and medium gamma ray argillaceous shale. High gamma ray siliceous shale and medium gamma ray siliceous shale are proved to be the best shale oil reserovir, which contains 2~8% of TOC, 2~12% of effective porosity, more than 50% of quartz content and high propotion of macropores.
The method proposed in this project has been implemented in many unconventional reservoirs in china to evaluate the resource potential and get a comprehensive understanding of the shale oil reservoir.
The wells tested based on the recommendation has got promising production after fracturing, which brought client big confidence for future exploration.
Hu, Zhenhua (PetroChina Liaohe Oilfield Company) | Zhang, Shenqin (PetroChina Qinghai Oilfield Company) | Wu, Fangfang (Schlumberger) | Liu, Xunqi (Schlumberger) | Wu, Jinlong (Schlumberger) | Li, Shenzhuan (Schlumberger) | Wang, Yuxi (Schlumberger) | Zhao, Xianran (Schlumberger) | Zhao, Haipeng (Schlumberger)
The igneous reservoir of Shahejie formation in eastern sag of Liaohe depression is characterized by complex geological environment, variable lithology and high heterogeneity. Reservoir evaluation is difficult only based on conventional logs due to complex lithology and pore structures. Effective igneous reservoirs were identified and reservoir controlling factors were analyzed based on effective porosity calculation, pore structure analysis, lithology identification, lithofacies analysis, fracture evaluation and heterogeneity analysis by combing nuclear magnetic resonance data, micro-resistivity image data, conventional logs as well as mud logging data.
Based on our study, the igneous reservoirs in the study area are more related with effective porosity and pore connectivity, and less related with fractures. Good reservoirs are mainly distributed on the top part of explosive facies and effusive facies, where lithologies are mainly Trachyte, volcanic breccia and breccia-bearing tuff. The weathering leaching process is quite important for igneous reservoirs, but the reservoir qulity would not be good if the weathering process is too strong as it will lead to low effective porosity.
The accuracy of igneous reservoir evaluation gets improved a lot by this integrated approach and the conclusion from this study will help to optimize igneous reservoire exploration plan.
Shang, Suogui (CNOOC, Tianjin Branch) | Gao, Kechao (CNOOC, Tianjin Branch) | Fan, Zhaoya (Schlumberger) | Chen, Jichao (Schlumberger) | Wu, Jinlong (Schlumberger) | Gao, Bei (Schlumberger) | Shi, Ning (Schlumberger)
As a result of a new exploration strategy focused on deep rather than shallow formations, the largest gas condensate reservoir has been discovered recently in Bohai Bay, China. Due to the complex fluid behaviour, appropriate characterization of the in-situ fluids and relevant flow testing are needed for the valuable insight they can give into gas condensate reservoir forecasting. This paper discusses a methodology for reliable production evaluation with the aid of in-situ fluid characterization and comprehensive well log analysis. The results are a key factor both to be fed into drillstem test (DST) design and equipment optimization for better production evaluation design and for an optimal production prediction for reserve booking. Based on the efficient and reliable productivity evaluation, we can make real-time decisions on whether to perform a DST, which DST equipment to use, where to do the test. In this paper, a new solution is proposed based on the problem elaborated above.
Hou, Yu Ting (Petrochina ChangQing Oilfield Company) | Xi, Sheng Li (Petrochina ChangQing Oilfield Company) | Wu, Yong (Petrochina ChangQing Oilfield Company) | Li, Huayang (Schlumberger) | Zhao, Xianran (Schlumberger) | Wu, Jinlong (Schlumberger) | Zhao, Haipeng (Schlumberger)
Unconventional reservoirs oil and gas resources have great potential for development, especially in North America, which has been successfully achieved commercial production. Shale oil is one of the unconventional resources. Most of the shale oil reservoirs have complex lithology, poor petrophysical characteristics, complex pore structure, and so on, especially for lacustrine shale oil formation. This paper describes an approach and workflow to characterize the Chang7-3 member shale oil reservoir in the Ordos basin, China by integrating the high tech digital rock physics core analysis data with other special core analysis data to calibrate the reservoir petrophysical properties. The special unconventional core analysis method taken for this project are Tight Reservoir Analysis technology (TRA), Thin Section scanning (TS scanning), Mercury Injection Capillary Pressure test (MICP), N2 and CO2 Gas sorption test, XRD and Nuclear Magnetic Resonance analysis (NMR), and the new logging technology employed are gamma ray spectroscopy logging (LithoScanner*), nuclear magnetic resonance logging (CMR*), dielectric logging (ADT*). The new core analysis and logging technology not only depict the characters of the shale oil reservoir from microscopic to macroscopic scale, but also guarantee to establish the accurate method for reservoir identification and evaluation. The data analysis from above led to the development of evaluation models for organic matter quality and reservoir quality. Analysis of the production data revealed that the hydrocarbon abundance of the Chang7-3 member lacustrine shale oil reservoir is controlled by both organic matter quality and reservoir quality. A production forecast chart of Chang7-3 Member lacustrine shale oil reservoir was constructed based on the organic matter quality and reservoir quality. The application of the developed methodology and workflow achieved very good results and is supported by the test data from multiple wells drilled in the study area.
Guantao oil reservoir of the Bohai Bay, is characterized by low formation water salinity, high pore structure heterogeneity and flooding, which complicates the logging response, especially the low contrast of resistivity response. Traditional methods by resistivity fail to estimate reservoir parameters accurately and cannot determine producible fluid type. In this study, the reservoir heterogeneity was investigated with advanced nuclear magnetic resonance data, and oil saturation was calculated using array dielectric data. Combining the two aspects, a special reservoir evaluation and fluid identification method was established.