摘要：Predictions of oil and gas reservoirs play an essential role in hydrocarbon reservoir exploration. Multi-attribute seismic data can provide abundant reservoir information. However, traditional reservoir prediction models only reflect the characteristics of local seismic channels, ignoring information on global geological spatial structures. Thus, a semi-supervised prediction model based on self-attention is proposed to extract reservoirs' oil and gas characteristics. With the high cost of labeled samples, neighborhood similarity measurement based on Grey Relation Analysis is applied to determine the homogeneity of adjacent seismic traces for the first time to obtain pseudo-labels while considering geological spatial structure. As a semi-supervised prediction model, a multilayer convolutional neural network with a multi-head self-attention mechanism is utilized, which can extract features from multiple dimensions and investigate global structural information. The experimental results demonstrate that the algorithm in this paper's reservoir oil and gas prediction is more accurate than that of conventional approaches and is consistent with real drilling data. ? 2023 Elsevier B.V.

ISSN号：2949-8910

卷、期、页：卷226

发表日期：2023-07-01

影响因子：0.000000

收录情况：EI（工程索引）

发表期刊名称：Geoenergy Science and Engineering

参与作者：Jiang, Wenbin,Zhang, Dongmei,Kang, Zhijiang,Jiang, Xinwei

第一作者：惠钢

论文类型：期刊论文

论文概要：Jiang, Wenbin,Zhang, Dongmei,Kang, Zhijiang,惠钢,Jiang, Xinwei，Research on seismic hydrocarbon prediction based on a self-attention semi-supervised model，Geoenergy Science and Engineering，2023，卷226

论文题目：Research on seismic hydrocarbon prediction based on a self-attention semi-supervised model