Identification of strategies to reduce accidents and losses in drilling industry by comprehensive HSE risk assessment—A case study in Iranian drilling industry

Providing a comprehensive approach to oil well blowout risk assessment

Drilling is one of the most dangerous activities in the oil and gas process industries. Therefore, a holistic approach was presented to prevent and control risks and reduce the uncertainty of blowouts, kick scenarios, and control layers based on the Fuzzy Bayesian Network (FBN). Four independent protection layers (IPLs) were used to evaluate the blowout outcome, and the failure probabilities of IPL1 and IPL2 were calculated with Fault Tree Analysis (FTA). Then, different events were transferred to the Bayesian Network (BN) in GeNIe software, and updated the probabilities. The Fuzzy Fault Tree (FFT) results reveal that the failure probabilities for IPL1 and IPL2 amount to 8.94×10-4 and 4.97×10-21, respectively. Also, the blowout probability results based on FFT were larger than FBN. According to FBN, the probability of the kick event was equal to 6.60×10-34. Sensitivity analysis showed that X1 (Mud volume/flow change) had the highest degree of importance in the blowout of oil wells. The results of this study can be used in both a preventive and reactive approach. Deductive and inductive reasoning, the dynamic nature and conditional dependencies, and causal relationships between events can make the model more realistic.

An intelligent moving window sparse principal component analysis-based case based reasoning for fault diagnosis: Case of the drilling process

The drilling process is an important step in petrochemical industries, but the drilling process is risky and costly. In order to improve the safety and cost the impact of faults in the drilling process, this paper proposes intelligent moving window based sparse principal component analysis (MWSPCA) integrating case-based reasoning (CBR) (MWSPCA-CBR) in the fault diagnosis of the drilling process in the petrochemical industry. Through introducing sparsity into the PCA model, the Lasso constraint function of the MWSPCA method is used to optimize the sparse principals. The corresponding T² and Q statistics calculated by the selected sparse principals decide whether the faults have occurred, and the occurrence time of the anomaly is quickly located based on the MWSPCA method. Then the CBR method is used to analyze the anomaly data to identify the possible fault types, and provide the relational handling methods for real-time monitoring experts. Finally, the MWSPCA method is verified based on the intelligent diagnosis of the Tennessee Eastman (TE) process, reducing false negatives and false positives and improving the accuracy rate and the diagnosis speed. Furthermore, the proposed method is applied to analyze the data of the drilling process. The experimental results demonstrate that the proposed method can effectively diagnosis faults in the drilling process and reduce risks and costs in the petrochemical industry.

A Risk Analysis‐Best Worst Method Based Model for Selection of the Most Appropriate Contract Strategy for Onshore Drilling Projects in the Iranian Petroleum Industry

The pre-project planning phase has a significant impact on the achievement of project objectives because during this stage, major decisions including involving contract strategies are made with a high degree of uncertainty. Studies show that the contract type can play a unique role in the achievement of project success. On the other hand, drilling projects can be considered as one of the most critical types of projects in the petroleum industry. In this research, a novel risk based best-worst method (risk-BWM) is proposed for solving the issue of selecting the best contract strategy. A three level methodology was designed; firstly, the risk breakdown structure (RBS) of drilling projects was created in four levels including one heading in level 0, eight main areas of risk in level 1, 34 sub-areas of risk in level 2, and finally, 217 risk items in level 3. Secondly and on the basis of BWM, the weights of risk factors were determined as the selection criteria and consequently the best and the worst criteria were specified. Finally, using pair-wise comparisons between six types of drilling prevalent in contracts, the most appropriate contract type was proposed. The contribution of this study is the development of a generic RBS for drilling projects and application of the risk factors for the first time for the selection of contract type using the BWM method, which has the potential of being adapted for other types of underground projects.