Deep learning-based method for predicting lithologic sequence model through using seismic data

Abstract

The invention discloses a deep learning-based method for predicting a lithologic sequence model through using seismic data. The method includes the following steps that: 1) the lithologic data of a target stratum section of a well in a work area and near-well trace seismic data are measured so as to be adopted as training data; 2) the near-well trace seismic data are normalized, so as to be converted to a range of -1 to 1; 3) a stacked recurrent neural network and a sequence-to-sequence recurrent neural network model are adopted to respectively train the processed near-well trace seismic dataand the well lithologic data, with the near-well trace seismic data adopted as observation data, and the well lithologic data adopted as target data, iterative computation is performed, so that a learning model reaches convergence; and 4) actual seismic data are inputted to the learning model which is obtained after calculation in the step 3), so that a predicted lithologic sequence is obtained. With the deep learning-based method for predicting the lithologic sequence model through using the seismic data of the invention adopted, a lithologic data body capable of effectively reflecting reservoir distribution can be generated under the control of a seismic data sequence, and an inter-well reservoir prediction problem can be solved, and a basis can be provided for exploration and development.

Description

technical field [0001] The invention relates to a deep learning-based method for predicting a lithology sequence model by using seismic data, belonging to the field of reservoir prediction for petroleum exploration and development. Background technique [0002] Lithology prediction is one of the important means of reservoir prediction. Reasonable lithology prediction results are helpful to carry out the analysis of sedimentary facies distribution and sedimentary evolution law, and then predict the spatial distribution of favorable reservoirs to guide exploration and development deployment. So far, the research on the identification and prediction methods of lithology has mainly focused on the relationship between well logging curves and lithology sequences, while less research has been done on the relationship between seismic waveforms and lithology sequences. On the one hand, it is limited by the limitations of available methods. The use of seismic data to predict lithology...

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