Application of principal component analysis on water flooding effect evaluation in natural edge-bottom water reservoir

Abstract

Water flooding effect evaluation is considered as the basic work to formulate comprehensive adjustment measures and improve the effectiveness of oilfield development. However, natural edge-bottom water energy is seldom considered in the conventional evaluation method. So, it cannot reflect the comprehensive effect of both natural edge-bottom water and injected water. Principal component analysis is a kind of multivariate statistical analysis method, which has been widely used in social science and other fields. Based on this method, the water flooding effect of 5 edge-bottom water reservoirs is comprehensively evaluated. First, 11 indicators are selected from four aspects, including natural edge-bottom water energy, production change, water injection development and utilization, energy maintenance and deficit compensation. Then, the selection of principal components is optimized. Based on the consideration of keeping as much information as possible to get more convincing results, three principal components are obtained. Finally, take five oilfields as examples to realize comprehensive evaluation. Results indicate that the natural energy of B oilfield is quite sufficient and water injection is timely in the later stage of development. So the water flooding effect is the best among five oilfields and the comprehensive principal component value is 1.434. That of A and C oilfields are 0.527 and 1.021, respectively, ranking 3 and 2. Although D oilfield has quite sufficient natural energy, water injection is not timely. So the water flooding effect is poor and the comprehensive principal component value is 0.259. That of E oilfield is − 3.241, indicating that it has the worst water flooding effect. The ranking results of five oilfields are consistent based on principal component analysis and Tong's chart, which are both B, C, A, D and E oilfield, verifying this method’s feasibility and practicability. Additionally, compared with the single index, it can reflect the comprehensive water flooding effect of both natural edge-bottom water and injected water. Specific oilfield cases are evaluated by the proposed method, which help for better understanding its application potential for evaluating the water flooding effect of natural edge-bottom water reservoirs.

Influence of Mounting Incline and Natural Environmental Factors on Pin-Type Silicone Rubber Insulator Leakage Currents Using Internet-Based Monitoring

“…This result indicates that among the remaining insulator leakage currents, the vertical mounting insulator leakage current was affected mainly by humidity. Nevertheless, these insulators are generally immune to temperature and humidity, which is in agreement with previous reports [8], [13], [17]. Finally, the rainfall parameter (R) was close to the insulator leakage currents and was installed at sixty-degree and horizontal mounting positions.…”

“…The variance reflects the contribution degree information, and for the two-dimensional plot (biplot), as Eq. ( 2), where β(2) is the first two cumulative variances of the contribution rate, and λ i are eigenvalues [17], [30], [31], [32], [33]:…”

“…The leakage currents of the insulators increased as the humidity [12], [13] and rainfall [12], [14] increased. Moreover, the increasing exposure duration caused the leakage currents to rise [1], [12], [13], [15], [16], [17], [18], [19], [20]. Nevertheless, this exposure decreases insulator resistance and weathering due to long-term ultraviolet exposure [4].…”

Drainage Type Classification and Key Controlling Factors of Productivity for CBM Wells in the Zheng Zhuang Area, Southern Qinshui Basin, North China

“…PCA is a form of multivariate analysis for transforming many indicators into relatively few principal components without a reduction in the original information. The PCA method can decrease the dimensionality of the variable space and overcome the challenge of an overlap of information between evaluation indices. , Based on the geological and engineering parameters of CBM wells in the study area, the controlling factors of productivity for these wells were extracted. Then, the drainage type of CBM wells was classified by synthesizing gas and water production data and the comprehensive scores obtained by PCA.…”