Applying artificial intelligence (AI) to data from Industrial Internet of Things (IIoT) devices is a novel direction in geological studies. However, privacy and security concerns hinder the sharing of data, thus affecting the performance of current AI-based approaches. In this article, we propose a novel data management style to address the privacy and security issues in joint hydrocarbon explorations. Federated learning can facilitate the analysis of multiple datasets without the need to share them, protecting private information of different companies in a virtual joint venture. We use the inference of petroleum reservoirs in karst stratigraphy as a case study. A federated learning-based enterprise data management framework is proposed to virtually integrate the information from different organizations. Our key contributions are summarized as follows. 1) A method for karst identification and inference is proposed, which uses neural networks to recognize the size of petroleum reservoirs in different karst areas. 2) A federated learning algorithm is applied to virtually aggregate data samples from different companies. 3) The performance of the new privacy-preserving integration model is compared with those of the individual/local deep learning models. Our results show that the proposed approach can substantially improve the accuracy of petroleum reservoir explorations.

We have addressed the seismic impedance inversion problem, which is often ill posed because of inaccurate and insufficient data. The approach taken is based on dictionary learning and sparse representation. By shifting a patch window of fixed size on all well-log data, a large number of small overlapping patches are generated. Then regarding these patches as a training set and using K-singular value decomposition algorithm, we obtain a dictionary that describes the common features of subsurface models within the current survey area. On the basis of the assumption that the subsurface geology has similarity and lateral continuity to some extent, the dictionary is used to approximate each model via sparse representations over the learned dictionary. In particular, we impose the sparse representations as additional constraints into the inversion procedure, leading to a well-defined objective function that can not only fit the observed seismic data but also honor the features of the well-log data. We adopt a coordinate descent strategy to solve this objective function. Meanwhile, to enforce lateral continuity in the inverted models, we use an additional stage in which we use the nonlocal similarity information that is extracted from seismic data as spatial coherent prior knowledge to refine the estimated models. Compared with several traditional impedance inversion methods, our algorithm can produce solutions of much higher quality qualitatively and quantitatively.

Geostatistical inversion is a technique widely used in seismic inversion. It builds the posterior distribution by incorporating prior information. Abundant prior information for geostatistical inversion is provided by the spatial variability of well log represented by variograms. However, a variogram can only describe the spatial variability of lithologic parameters at a single point. Describing the structural relationship of rock deposition in the vertical direction (the sedimentary rhythm) requires a continuous curve, rather than one single point. We have developed a geostatistical seismic inversion method constrained by sedimentary structural features to enhance the consistency between inversion results with the sedimentary features of a reservoir. This method first uses sparse representation to learn the sedimentary structural features of a reservoir. Then, the structural features are taken as prior information of geostatistical inversion to improve the accuracy of inversion results. The novelty of our method is that the element describing the spatial variability of model parameters is extended from a single point to a sedimentary structural block. We derive an analytical expression of posterior distribution of model parameters with constraints of sedimentary structural features and realize a data-driven geostatistical seismic inversion. Experiments on synthetic and field data indicate promising results consistent with the sedimentary features of reservoir.

Fault surface extraction plays a vital role in structural interpretation and structural modeling, which can enhance our understanding of geologic structures. Significant effort has been invested in fault surface extraction in the past few years. These contributions can be roughly divided into two types: one constructs fault sticks or fault patches and links them to form fault surfaces, and the other constructs a complete fault surface by roughly determining the location of a fault. The former is likely to extract incomplete fault surfaces in areas with low data quality or complex fault structures. The latter has problems dealing with faults in the case of complex fault structures, such as intersecting faults. To effectively address these problems, we have developed a novel fault extraction workflow from a global perspective, which can extract fault surfaces on the premise of obtaining the fault distribution of the entire data set. First, we introduce a multilayer complex network to characterize the overall distribution of faults, in which nodes represent faults, node attributes cover information, such as the location of the faults, and edge attributes give the relationship between faults. Then, for each fault represented by the node, we develop a fault surface extraction method based on computational topology, which can guarantee the completeness of the fault surfaces. We apply our method to synthetic data and field seismic data to evaluate the method quantitatively and qualitatively, respectively. The results indicate that the proposed approach extracts the fault effectively, ensures the completeness of the fault surface, and has high performance in areas with complex faults. Thus, the approach can work well in complex situations, such as intersecting faults.

Seismic inversion accuracy significantly affects the quality of reservoir modeling. Given the limited samples of well logging, prior information such as geological data and stratigraphic characteristics is crucial for enhancing the accuracy and reliability of inversion results. The inability to fully exploit prior information in the data to compensate for data deficiencies may impair network performance. Moreover, the traditional artificial neural networks (ANNs) cannot fully exploit prior information in the data to compensate for data deficiencies, which may impair network performance and further leads to the network’s limited ability to effectively assimilate external prior knowledge. Therefore, a prestack seismic inversion driven by prior information neural network (PINN) and statistical characteristic is proposed to integrate various geological prior information. Initially, lithological analysis is conducted on the well logging data, leading to the classification of the strata into a series of sub-layers based on lithology. After obtaining the statistical characteristics of thickness and elastic parameters for each sub-layer, a geostatistical algorithm is employed to generate numerous pseudo-wells that conform to actual sedimentary laws. Subsequently, the PINN is pre-trained using substantial samples, encompassing both pseudo and real well logging data, to integrate geological structural prior information into its network parameters. Lastly, PINN served as a specially designed prior constraint for the inversion network, allowing its geological information to effectively guide the inversion process via backpropagation. This inversion method is applied to both synthetic and field examples, and in comparison to conventional inversion algorithms, it demonstrated superior accuracy in blind well verification.