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.
For fractured gas reservoirs with strong heterogeneity, aqueous and gas phase trapping affect the gas recovery seriously. The recovery may be less than 50% for some reservoirs while it is only 12% for Beaver River gas field. The aqueous and gas phase trapping mechanism has been revealed by the results of depletion experimental test by using core samples with spontaneous imbibition. The residual pressure is as high as 11.75MPa when a 12.8cm imbibition layer set in the depletion test, resulting in gas recovery deceased 49.5% compared with that without imbibition layer. A mathematical model has been established to exhibit the imbibition thickness. The aqueous and gas phase trapping are analyzed by two representative wells in Weiyuan gas field, the extremely low gas recovery can be explained more rationally. That lays a foundation of improving the gas recovery for fractured reservoirs.
In order to reduce the cost of wellheads, the production rate of the gas wells in the Hechuan Gas Field are mostly measured in groups, which raises a stringent barrier for industries to determine the production rate of each single well. The technique for determining the production of a single well from the production of the well-group can be called the production splitting method (PSM). In this work, we proposed a novel PSM for the multi-well-monitor system (MWMS) on the basis of the Beggs and Brill (BB) correlation. This proposed method can account for the multi-phase flow together with the features of the pipelines. Specifically, we discretize the pipeline into small segments and recognize the flow pattern in each segment. The pressure drop along the pipeline is calculated with the Beggs and Brill correlation, and the production of each well is subsequently determined with a trial method. We also applied this proposed method to a field case, and the calculated results show that the results from this work undergo an excellent agreement with the field data.
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