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CO2-flooding can enhance crude oil recovery rates by reducing the viscosity of crude oil, improving the mobility ratio between crude oil and water, and leveraging the swelling effects of crude oil components. Furthermore, it facilitates the sequestration of greenhouse gases, leading to the realization of carbon capture, utilization, and storage (CCUS), which holds significant strategic importance for mitigating climate change, safeguarding ecological environments, and achieving sustainable development. Consequently, predicting CO2-flooding performance in heterogeneous low-permeability reservoirs holds crucial significance in sustainable energy production. However, the drawbacks of numerical simulation techniques, such as extensive computational resource consumption and prolonged simulation durations, have prompted extensive research into the utilization of artificial intelligence technologies. Prevalent approaches often treat the problem as a time series issue, which proves inadequate for reservoirs lacking historical production data. Furthermore, this methodology is unable to fully exploit the complex implicit relationships between various factors and the prediction target. Our study proposes a novel method for predicting the performance, which stands apart from previous approaches by not treating the problem as a time series issue, eliminating the need for historical production data for each prediction, and can predict various production variables such as gas production, oil production, water production, etc. in a universal manner. We combine enhanced MultiScale Non-local Neural Networks with ConvNet to handle sparse well network matrices with injection-production parameters. Additionally, we employ MultiScale convolutional networks with spatial attention mechanism to address formation permeability fields with spatial distribution features. Experimental results demonstrate the superiority of our approach in accuracy, achieving a prediction accuracy of over 97%. This research provides an innovative and flexible method, making significant contributions to the intelligentization and sustainable development of the petroleum industry, aligned with global efforts to both reduce carbon emissions and neutralize the existing ones.

ABSTRACTNull dereferences are a bane of programming in languages such as C, and static analysis is an effective method to detect them but often due to false negative. This paper proposes a points-to property sound analysis method to detect all potential null dereferences. First, the feature and detection method of null dereference of C programs are introduced. Second, an abstract memory model Region-based Symbolic Three-Valued Logic (RSTVL) is proposed to describe storage states of memory objects. Third, the property sound analysis theory based on abstract interpretation is proposed, which only analyze a single property of a program applies over-approximation strategy. Then, evaluations’ rules of points-to property based on RSTVL are presented, which guarantees the points-to property of a pointer contains the actual points-to property and detect all potential null dereferences. Experiment results of five real projects show that this method could detect all potential null dereferences with acceptable false-positive rates and efficiency.

Abstract memory model plays an important role in the static analysis of programs. This paper proposes a region-based symbolic three-valued logic (RSTVL) to guarantee the soundness of static analysis, which utilises abstract regions to simulate blocks of the concrete memory. RSTVL applies symbolic expressions to express the value of memory objects, and the interval domain to describe the value of each symbol of symbolic expressions. Various operations for memory objects can be mapped to operations about regions. As a sound abstract memory model, RSTVL can describe the shape information of data structure in memory and the storage state of memory objects for C programs, and a variety of associative addressable expressions, including the point-to relations, hierarchical and valued logic relations. We have built a prototype tool DTSC_RSTVL that detects code level defects in C programs. Five popular C programs are analysed, the results indicate that the analysis is sufficiently sound to detect code level defects with zero false negative rate.