Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot

Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation.

The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Although hepatitis B is widespread, it is hard to cure. This paper presents a new and more accurate model for the diagnosis and treatment of hepatitis B. Based on previous research, the diagnosis and treatment modes were combined into one. By adding more influencing factors and risk factors, the overall diagnosis and treatment model will be further expanded, and a richer and more detailed overall diagnosis and treatment model will be constructed. Reverse logic gates are used in the model to improve the accuracy of the treatment planning. The new unified model is more accurate in subdividing diagnosis results, and it is more flexible and accurate in providing dynamic treatment plans. The prediction process and the static diagnosis process of the model are symmetric, and the related sub-graph is symmetric in structure. In addition, an algorithm for predicting the response probability of treatment scheme is developed, so as to predict the subsequent treatment effects of the current treatment scheme, such as the probability of drug resistance. The results show that this method is more accurate than other available systems, and it has encouraging diagnostic accuracy and effectiveness, which provides a promising help for doctors in diagnosing hepatitis B.

A method for root cause diagnosis with picture fuzzy sets based dynamic uncertain causality graph

Root cause diagnosis is of great significance to make efficient decisions in industrial production processes. It is a procedure of fusing knowledge, such as empirical knowledge, process knowledge, and mechanism knowledge. However, it is insufficient and low reliability of cause analysis methods by using crisp values or fuzzy numbers to represent uncertain knowledge. Therefore, a dynamic uncertain causality graph model (DUCG) based on picture fuzzy set (PFS) is proposed to address the problem of uncertain knowledge representation and reasoning. It combines the PFS with DUCG model to express expert doubtful ideas in a complex system. Then, a new PFS operator is introduced to characterize the importance of factors and connections among various information. Moreover, an enhanced knowledge reasoning algorithm is developed based on the PFS operators to resolve causal inference problems. Finally, a numerical example illustrates the effectiveness of the method, and the results show that the proposed model is more reliable and flexible than the existing models.

Stochastic Simulation Method for Reasoning of Dynamic Uncertain Causality Graph (DUCG)

Dynamic Uncertain Causality Graph (DUCG) is an innovative model developed recently on the basis of dynamic causality diagram (DCD) model, which has been proved to be reliable for fault diagnosis of nuclear power plants. DUCG can represent complex uncertain causal relationship graphically, with both high efficient inference and support of incomplete expression. Therefore, DUCG is often built much larger than Bayesian Network (BN). However, as the scale of real problem is so large, DUCG still has the problem of combination explosion. Stochastic Simulation is a common solution for it. However, it is almost impossible to use traditional sampling algorithms for DUCG because the joint probability of evidences could be less than 10−20. In this paper, the algorithm based on conditional stochastic simulation for the inference of DUCG was proposed. It obtains the probability of evidences by calculating the expectation of the conditional probability in sampling process instead of using the sampling frequency, which overcomes the difficulty. What’s more, this algorithm uses recursive reasoning method of DUCG to calculate conditional probability distributions of node for sampling, which means this process only depends on its parent nodes’ states. As a result, the algorithm features in lower time complexity. In addition, it has the potential of parallelization like other sampling algorithms. In conclusion, this algorithm is promising to provide a new solution to the inference of the DUCG in large-scale and complex state situations.