Fuzzy AI Model Promises Smarter Risk Prediction for Oil and Gas Pipelines

Pipeline networks are the hidden arteries of the global energy system, quietly moving crude oil, produced water, and refined hydrocarbons across thousands of kilometers of terrain every day. When these systems fail, the consequences can be catastrophic: loss of containment, environmental devastation, financial ruin, and in the worst cases, loss of human life. History offers sobering reminders, from the Macondo well blowout that drove a leading oil and gas company into bankruptcy to countless smaller corrosion-related leaks that quietly erode both profits and public trust. Now, a team of researchers from Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia, has developed a hybrid artificial intelligence framework that promises to make pipeline risk assessment dramatically more accurate, more transparent, and more honest about its own uncertainty.

The study, published in the journal Cleaner Engineering and Technology, introduces an intuitionistic fuzzy deep neural network (IFDNN) method designed to overcome one of the most stubborn problems in industrial safety engineering: uncertainty. Traditional risk assessment models, however sophisticated, ultimately force messy, incomplete, and subjective real-world information into neat numerical categories. The researchers argue that this simplification is precisely where safety margins are lost, because the world of pipeline integrity is governed not by crisp numbers but by vague expert judgments, sparse inspection records, and damage mechanisms that evolve in ways no single probability distribution can capture.

Lead author Tri Wahono and colleagues Imam Mukhlash, Endah R.M. Putri, and Agung Purniawan built their model on a mathematical foundation first laid out in 1986, when mathematician Krassimir Atanassov extended classical fuzzy set theory. Ordinary fuzzy logic, pioneered by Lotfi Zadeh in 1965, allows an element to belong to a set to a partial degree, capturing the vagueness of linguistic judgments such as “corrosion risk is fairly high.” Intuitionistic fuzzy sets go one step further by assigning each element both a membership degree and a non-membership degree, with the gap between them representing an explicit hesitation component. This three-part structure means the model does not merely say how strongly a factor contributes to risk, but also how strongly it does not, and how much doubt remains in between.

The new framework unfolds in three carefully sequenced stages. First, the team anchored their approach in two established industry standards, the Kent Muhlbauer pipeline risk model and the American Petroleum Institute’s RP 581 risk-based inspection methodology, which together organize pipeline threats into eight main factors and thirty-one sub-factors. Failure probability is evaluated against corrosion, third-party damage, sabotage, incorrect operation, and geohazards, while consequences are assessed across safety, environmental, and financial dimensions, each rated on a scale from one to five. In the second stage, these numerical ratings are transformed into triangular intuitionistic fuzzy numbers through a fuzzification process, allowing every judgment about the pipeline to carry its own envelope of uncertainty.

At the heart of the method sits an intuitionistic fuzzy inference system built on the Mamdani architecture, which propagates fuzzy, rather than fixed, output values through a rule base of conditional statements. For the probability of failure alone, combining five input factors across five linguistic categories generates 3,125 distinct intuitionistic fuzzy implications, while the three consequence factors generate another 125. Each rule computes a membership degree through a sup-min operator and a non-membership degree through an inf-max operator, aggregating thousands of rules into a coherent fuzzy picture of risk. A tunable defuzzification operator then converts these fuzzy outputs back into crisp numbers, with a parameter that lets analysts decide how sensitive the final risk score should be to lingering uncertainty.

The third stage is where deep learning enters. The intuitionistic fuzzy membership and non-membership degrees produced by the inference system become the training inputs for a deep neural network constructed in TensorFlow-Keras. After systematic experimentation, the researchers settled on a feedforward architecture with four fully connected hidden layers of 128, 64, 32, and 16 neurons, using the Gaussian error linear unit (GELU) activation function, which outperformed ReLU and SiLU alternatives. Trained with the Adam optimizer on data split into 70 percent training, 20 percent testing, and 10 percent validation subsets, the network achieved an R-squared of 0.99443 with a mean squared error of just 0.000101, performance the authors describe as robust across training, testing, and validation data alike.

The model was not tested on toy data. The case study drew on approximately 1,750 kilometers of onshore upstream oil and gas pipelines spread across 15 operational fields, divided into 19,405 sub-segments. Diameters ranged from 4 to 40 inches and asset ages from under a decade to more than half a century, capturing the full diversity of a mature pipeline estate, including aging networks near populated areas where decades of corrosion pose genuine integrity concerns. The network transports produced fluids from wells to processing facilities, produced water to injection wells, and crude oil to storage and refinery units, providing exactly the kind of heterogeneous operating conditions that break simpler models.

The comparative results are striking. Where the traditional Muhlbauer-based model classified the first of three representative pipeline segments as high risk, the intuitionistic fuzzy deep neural network downgraded it to medium risk after properly weighing the relative importance of each contributing factor. Segment 2 remained medium risk, while Segment 3, an aging transmission line near public and environmentally sensitive areas, was consistently flagged as highest risk by all three approaches. Crucially, the risk prioritization order survived the transition to the new model, with Segment 3 ranking above Segment 1 and Segment 2 last, demonstrating that the hybrid method sharpens resolution without distorting the underlying risk landscape. Corrosion emerged as the dominant driver of failure probability, pointing operators toward targeted interventions: high-resolution internal inspection, chemical injection programs suited to specific mechanisms such as microbiologically influenced corrosion or CO2 corrosion, protective coatings, and repair or replacement of sections failing fit-for-service criteria.

The practical implications extend well beyond academic benchmarking. Because risk-based inspection concentrates resources where they matter most, a model that more accurately distinguishes high-risk from low-risk equipment translates directly into fewer unnecessary inspections, longer intervals between interventions on genuinely safe segments, and lower lifecycle costs without sacrificing safety. The researchers emphasize that overestimating risk is expensive in its own right, since inspecting low-risk equipment more frequently drains budgets that could be protecting genuinely dangerous assets. They envision the framework transforming fixed-cycle inspection plans into predictive, risk-informed maintenance programs, though they caution that successful deployment requires user training, data validation, phased integration, and pilot testing. Limitations remain: the model has so far been validated only on onshore pipelines, does not yet capture interactions among multiple simultaneous threats, and would benefit from integration with real-time monitoring for dynamic risk prediction. Extending the framework to offshore systems and to the tangled dependencies among corrosion, mechanical damage, and material defects stands as the team’s next frontier in the ongoing effort to keep the world’s energy arteries safe.

Subject of Research: A hybrid intuitionistic fuzzy deep neural network method for risk assessment of oil and gas pipelines

Article Title: An intuitionistic fuzzy deep neural network method for risk assessment of oil and gas pipelines

Article References: Wahono, T., Mukhlash, I., Putri, E. R., & Purniawan, A. (2026). An intuitionistic fuzzy deep neural network method for risk assessment of oil and gas pipelines. Cleaner Engineering and Technology, 34, Article 101299. https://doi.org/10.1016/j.clet.2026.101299

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101299

Keywords: oil and gas pipelines, risk assessment, intuitionistic fuzzy sets, deep neural networks, risk-based inspection, pipeline integrity management, artificial intelligence, corrosion, fuzzy logic, uncertainty modeling, machine learning, asset integrity