Abstract
Fault imaging follows the processing and migration imaging of seismic data, which is very important in oil and gas exploration and development. Conventional fault imaging methods are easily influenced by seismic data and interpreters’ experience and have limited ability to identify complex fault areas and micro-faults. Conventional convolutional neural network uniformly processes feature maps of the same layer, resulting in the same receptive field of the neural network in the same layer and relatively single local information obtained, which is not conducive to the imaging of multi-scale faults. To solve this problem, our research proposes a modified U-Net architecture. Two functional modules containing dilated convolution are added between the encoder and decoder to enhance the network’s ability to select multi-scale information, enhance the consistency between the receptive field and the target region of fault recognition, and finally improve the identification ability of micro-faults. Training on synthetic seismic data and testing on real data were carried out using the modified U-Net. The actual fault imaging shows that the proposed scheme has certain advantages.
Highlights
IntroductionFaults play a major role in lateral sealing of thin reservoirs and accumulation of the remaining oil in conventional and unconventional reservoirs onshore in China [1]
Almost all developed onshore oil and gas fields in China are distributed in rift basins which are rich in oil and gas resources with highly developed and very complex fault systems [2–4]
This paper introduces a new neural network model, which takes U-Net as the basic network and uses inter-group channel dilated convolution module (GCM) to connect each cross-connection layer between encoding path and decoding path, and uses inter-group space dilated convolution module (GSM) to connect layers after each deconvolution layer in decoding path
Summary
Faults play a major role in lateral sealing of thin reservoirs and accumulation of the remaining oil in conventional and unconventional reservoirs onshore in China [1]. It is very reasonable to attribute fault identification via deep learning to the research field of seismic data processing and imaging, and it is the development trend in the future. With the continuous down-sampling of the network and the convolution operation with step size, the defect that only a single size information can be obtained at the same layer becomes more and more obvious, resulting in the inaccurate identification of faults by the neural network To address these issues, this paper introduces a new neural network model, which takes U-Net as the basic network and uses inter-group channel dilated convolution module (GCM) to connect each cross-connection layer between encoding path and decoding path, and uses inter-group space dilated convolution module (GSM) to connect layers after each deconvolution layer in decoding path. Tcohnevseinmtiiolanraitlycobnevtwoleuetniodnillaietsedinctohnavtotlhuetisoizneaonfdcoconnvvoelunttiioonnaklecrnoenlviosltuhteiosnamliees, itnhatthiast, tthhee sniuzemobfecroonfvpoalruatmionetekresrnoeflniesutrhael snaemtwe,otrhkarteims,athinesnuunmchbaenrgoefdp[a2r2a,m23e]t.eTrsheofdnifefuerreanl cneetlwiesorink rthematadinilastuedncchoannvgoeludti[o2n2,h2a3s].aTlahregedrifrfeecreepntcievelifeiselidnatnhdatcadnilaptreedsecrovnevthoelusttirouncthuarse aoflainrtgeerrrneacledpatitvae[2fi4e,2ld5]a. nd can preserve the structure of internal data [24,25]
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