New applications of low-frequency absorption attenuation gradient analysis for potential evaluation in oilfield development stage

Zhang, Pengzhi; Chen, Huajing; Bai, Qinyun; Wang, Hong

doi:10.1190/segam2019-3215611.1

Cited by 1 publication

(1 citation statement)

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“…Terefore, the relationship of this type of reservoir is more complex, and it is difcult to realize the fne modeling of fault block reservoir structure, which leads to the signifcant increase in development difculty and the deviation of the overall development efect of the reservoir. Previously, based on practical experience and theoretical derivation, oilfeld workers proposed a variety of traditional methods for development potential evaluation, mainly including well pattern density method, numerical simulation method, water drive curve method, production decline method, and prediction model method [3,4]. However, in the practical application of the abovementioned methods, due to the complex boundary conditions and oil-water distribution of the fault block reservoir, there will be a large deviation between the prediction results and the real situation.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Fault Block Reservoir Measure Index Based on 1DCNN-LightGBM

Wang

Zhang

et al. 2023

Scientific Programming

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In view of the shortcomings of the prediction method of future development measures and indicators of fault block reservoir in the current oilfield practical application, a prediction method of fault block reservoir measures and indicators based on the random forest method and LightGBM is proposed, which can help the oilfield make more effective decisions in the middle and later development. Firstly, using the advantages of random forest (RF) in dealing with high-dimensional data sets, the main controlling factors are selected by feature analysis. Then, the measure prediction model is established by using the 1DCNN-LightGBM algorithm. Firstly, 1DCNN processes the reservoir dynamic data and then trains the LightGBM model with the extracted time series characteristics and static data characteristics as input to predict the measure indexes of fault block reservoir. The evaluation results show that the prediction models proposed in this paper have good performance and can obtain more accurate prediction results and more stable prediction performance. It provides a basis for the future planning and optimization of the oilfield.

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Section: Introductionmentioning

confidence: 99%