As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved.
Summary
The in-situ reservoir status monitoring plays a critical role in natural gas hydrate resource production. Considering the complexity of the field environment, a simulation framework for monitoring gas hydrates with cross-hole electrical resistivity tomography (CHERT) was developed to monitor the hydrate distribution during hydrate formation and dissociation. The simulation study comprised both numerical and physical experiments. The optimal CHERT array was designed through a numerical experiment. The effect of applying CHERT was verified through a physical experiment (a high-resistivity medium and hydrate formation experiment). The results show that improper electrode layouts will lead to varying degrees of low amplitude and blur boundary. An optimal CHERT array of a 100-mm electrode rod spacing, 8-mm electrode ring spacing and 48 electrode rings was obtained. The inversion results obtained using this CHERT array scheme can easily distinguish the distribution of high-resistivity targets and yield satisfactory results in hydrate formation experiments. These findings guarantee data processing and interpretation for applying CHERT in gas hydrate experiments and fields.
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