Optimizing image-based deep learning for energy geoscience via an effortless end-to-end approach

Seismic volumetric dip estimation via a supervised deep learning model by integrating realistic synthetic data sets

System software for interactive human–computer data processing based on the IASP91 Earth model was designed. An interactive data processing system for visualizing earthquake data was designed and implemented via the Intel Fortran platform. The system reads and processes broadband seismic data acquired by field stations, mainly including the reading and import of raw data, pre-processing, identification of seismic phases and inter-correlation traveltimes picking. In the data processing step, shortcomings have been improved and functions have been gradually refined and enhanced, making it easier and faster to process data. It has already processed more than 1000 large seismic events received by the station from 2013 to 2018. The practical application shows that the human–computer interaction system is easy to operate, accurate, fast and flexible, and is an effective tool for processing seismic data.

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An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing

As an essential process in subsurface interpretation, seismic‐well tie aims at calibrating the well measurements in depth domain with the seismic records in time domain for reliable reservoir mapping and modelling. Such a process usually requires massive manual efforts, primarily extracting source wavelets to synthesize seismograms from well measurements and stretching/squeezing synthetic seismograms to match actual seismic signals, and thus becomes time‐consuming and labour intensive with the amount of wells expanding in a field. This paper formulates the seismic‐well tie problem from the perspective of computer vision and, considering the lack of manually prepared training labels in most of real projects, proposes automating it by a self‐supervised workflow of two key components. The first component is to extract time‐variant wavelets in the target interval using a dual‐task autoencoder, which is optimized by maximizing the spectrum similarity between the extracted wavelet and the actual seismic. The second component is to estimate the corresponding time‐shift between a pair of the synthetic seismogram and the actual seismic using a flow net, which is trained by a pseudo dataset derived from the actual well measurements. More specifically, the data generation consists with defining one‐dimensional time‐shift curves to warp the original less accurate time‐depth relationships and then synthesizing the corresponding seismograms based on the convolutional model at all available wells. When turning to the stage of inference at a target well, the proposed workflow automatically extracts a representative wavelet, generates the corresponding synthetic seismogram from the original time‐depth relationship and estimates the time‐shift curve necessary for revising the original time‐depth relationship that would lead to an optimal tying with the actual seismic at the well. The proposed workflow is tested on two field datasets, and both results demonstrate improved tying over traditional methods such as statistical wavelet extraction and dynamic time warping.

Automating seismic‐well tie via self‐supervised learning


 Seismic wavelet extraction is a fundamental but crucial component in seismic data analysis, which aims at estimating the source wavelet for better decoding the subsurface reflectivities from seismic signals and calibrating the measurements between logging and seismic. Due to the rock heterogeneity, however, seismic energy attenuates during its propagation, and correspondingly the seismic wavelet is considered varying from shallow to deep as well as from near to far offsets, causing the wavelet time-variant. While machine learning (ML) appears feasible for assisting the challenge, without a comprehensive understanding of the seismic propagation, representative training labels cannot be prepared, and supervised learning appears less applicable. This study proposes implementing self-supervised learning into time-variant seismic wavelet extraction. Specifically, the proposed network is in the architecture of a dual-task auto-encoder (DTAE). Starting from 1-D seismic amplitude, the DTAE first uses an encoder to extract a set of features at multiple scales, which are then split into two flows, with one through a decoder that aims at reconstructing the input 1D seismic signal and the other through a few convolutional layers that aim at matching the spectrum between seismic trace and extracted wavelet. Correspondingly, the objective function of training such DTAE consists of two parts, the mean-square-error of the amplitude reconstruction and the mean-square-error of the spectrum matching. The proposed workflow is tested on various field datasets, and compared to the statistical wavelets, the extracted wavelets are of better match with the spectrum variation of seismic from shallow to deep and from near offset to far offset.

Hao Wu

Papers

Improved seismic well tie by integrating variable-size window resampling with well-tie net