Sensitivity analysis of log interpretation parameters of through casing reservoir saturation logging. (November 2022)

• Record Type: Journal Article
• Authors: Wang, Qincong; Xv, Zhengyao; Wang, Huawei; Li, Xincheng; Tian, Longmei; Yuan, Shixiong; Deng, Rui
• Is Part Of: Energy reports. Volume 8(2022)Supplement 12
• Page Start: 392
• Page End: 400
• Publication Date: 2022-11
• Subjects: Casing saturation -- Nuclear logging -- Log interpretation -- Sensitivity -- Monte Carlo; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.330
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In the middle and late stages of oilfield production and development, the increase of production measures has resulted in a greater difference in reservoir physical properties between layers and a complex oil-water relationship. Most of the main reservoirs are highly flooded, the distribution of formation water salinity is complex, and the accuracy of remaining oil evaluation is relatively low. In order to solve the problem of inaccurate evaluation of remaining oil in high temperature, high pressure, low formation water salinity and low porosity reservoirs, it is necessary to improve the existing logging interpretation and evaluation technology of remaining oil saturation. It can provide a powerful basis for accurately finding oil layers with development potential, and for adjustment and judgment of potential layers in the middle and late stages of oilfield development. In this paper, the parameter sensitivity is calculated by normalizing the parameter change amplitude, which can better compare the sensitivity of water saturation interpretation of each parameter at different depths. It can also be obtained which factor has the greatest influence on the interpretation of water saturation at different depths. Calculating the partial derivative value corresponding to the capture cross-section value of shale and formation water can specifically compare the sensitivity of parameters to the results and find out the direction of parameter adjustment.