Safety Evaluation of Oil Drilling Rig System by the Extension Theory and Analytic Hierarchy Process

Zhao, Xiaojiao; Qu, Zhan; Zhao, Hui-bo; Fan, Honghai

doi:10.1007/978-981-15-0860-8_13

Cited by 2 publications

(3 citation statements)

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“…In the context of safety assessment, fuzzy mathematics provides a framework for addressing ambiguous or imprecisely defined data and concepts. For example, extension theory [20], variable fuzzy theory [21] [22], set pair analysis [23], and cloud models [13] have been applied…”

Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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Crane operation hazard evaluation plays an important role in theo prevention of accidents and casualties. Therefore, formulating effective measures to ensure the safety of cranes is vital. However, most studies on crane safety evaluation overlook the risk attitudes of decision makers. To overcome the limitations of the current research, this study proposes a novel risk prioritization method based on the Znumber and cumulative prospect theory (CPT) for crane operation hazard evaluation. First, considering the uncertainty and unreliability of decision makers' assessments, the Z-number is utilized to capture the decision information with regard to crane operation hazards. Second, to consider decision makers' psychological behavior in crane operation hazard evaluation, CPT is used in the Z-environment to rank the identified hazards. Third, grade discrimination rules and cumulative prospect values are integrated to classify the danger level of a hazard. Finally, a case study involving a crane operation hazard evaluation based on the Z-number and CPT is employed to verify the practicability of the proposed model. Sensitivity analyses and comparative studies are conducted to validate the proposed method.

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Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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“…A comprehensive evaluation of drilling safety is the foundation for creating an effective drilling plan. To this end, several approaches have been utilized to evaluate the pre-drilling safety level, including Analytic Hierarchy Process (AHP), Fuzzy Evaluation, Rough Set Theory, Artificial Neural Network (ANN), Bayesian Network, Machine Learning and Deep-Learning Method [3,4,7,[18][19][20][21][22]. Of these methods, AHP is the basis for constructing an indicator system and is used to connect directly observed factors (e.g., formation type, temperature and pressure) to a pre-drilling safety level.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the closer the drilling safety value is to 0, the more dangerous the pre-drilling. The member-functions of the nine observed variables and the drilling safety level are established on the basis of the related literature and knowledge of experienced experts, and are described in detail as follows [18,27]:…”

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confidence: 99%

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

Fan,

Sun,

et al. 2024

Processes

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In order to avoid potential personnel and financial losses, the evaluation of pre-drilling safety is of great importance in oil and gas exploration and development. This paper presents a method of evaluating pre-drilling safety through combining the Analytic Hierarchy Process (AHP) with the Alternating Condition Expectation (ACE) method. An indicator system with a 9-3-1 structure was established, incorporating various unrestricted variables to describe the technical factor. Additionally, nine membership functions and weights were determined in order to build the AHP model by connecting the independent variables in the basic layer to dependent variables in the middle layer. Four transformed functions were also formulated to construct the ACE model by linking the middle variables to the pre-drilling safety value in the final layer. A total of 28 sets of on-site drilling data from three oilfields were collected for the establishment and verification of the AHP-ACE model. Average absolute error (AAE) and average absolute relative error (AARE) of the model to predict the training data are 0.03 and 4.29%, respectively, whereas the AAE and AARE for verification samples are 0.03 and 4.51%, respectively. The sensitivity ranking of the three potential variables is as follows: human factor exhibits the highest degree of sensitivity, followed by natural factor and technical factor, in descending order. The AHP-ACE model for pre-drilling safety assessment faces limitations in universal applicability and scope, particularly in real-time drilling activities. However, its potential for improvement lies in integrating insights from past operations and expanding the dataset to enhance accuracy and broaden safety assessment coverage. This method is not limited by blocks, which is of great significance to ensure drilling safety.

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Safety Evaluation of Oil Drilling Rig System by the Extension Theory and Analytic Hierarchy Process

Cited by 2 publications

References 0 publications

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

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