A Review of Intelligent Methods for Environmental Risk Identification in Polar Drilling and Well Completion

Abstract

The Arctic region is rich in oil and gas resources and has great potential for development. It has become a new hot spot for international development. However, the harsh climatic and geological conditions and fragile ecosystems in the Arctic region put forward stringent technical requirements for oil and gas development. Polar permafrost has an impact on the growth of plant roots and the absorption of water. When drilling activities are carried out, the permafrost layer may be broken, resulting in the erosion of polar soil and disorder of the water balance, thus affecting local vegetation and ecosystems. Moreover, the legal system of polar environmental protection is lacking, and it is necessary to form a perfect risk assessment method to improve the relevant laws and regulations. Therefore, it is very important to study the environmental risk identification technology for polar drilling. For polar drilling, it is necessary to establish a risk source classification and identification method for environmental pollution events. However, at present, it mainly faces the following challenges: poor polar environment, lack of monitoring data, and lack of a legal system for polar environmental protection. By systematically discussing risk identification technology, the application and applicable models of different types of risk evaluation methods are categorized and summarized, the advantages and disadvantages of different types of risk evaluation methods and their application effects are analyzed based on the unique environment of the polar regions, and then the development direction of the future environmental risk identification technology for polar drilling is proposed. In order to accelerate the development of polar drilling environmental risk identification technology, research should be focused on the following three aspects: ① Promoting the multi-dimensional integration of polar drilling environmental pollution index data, to make up for the short board of less relevant data in the polar region. ② Combining the machine modeling algorithm with risk evaluation of polar drilling environmental pollution to improve the scientificity and accuracy of the evaluation results. ③ Establishing a scientific and accurate polar drilling environmental pollution risk identification system to reduce pollution risk.

1. Introduction

The development potential of oil and gas resources in the polar region is huge, and the distribution of oil and gas resources in the Arctic region is shown in Figure 1 [1]. According to statistics, the current proven conventional oil and gas resources in the Arctic region exceed 400 × ${10}^8 {\mathrm{m}}^3$ [2,3,4,5]. The total amount of traditional oil and gas resources that are not fully proven is about 412.2 billion barrels of oil equivalent, and natural gas and liquefied natural gas production accounts for 80%, while oil reserves are 91 billion barrels, natural gas is 48 trillion cubic meters, and liquefied natural gas is 45 billion barrels [6]. Due to the development needs of future energy strategies, polar oil and gas resources have become the focus of attention of major oil companies and countries. Russia expects that its Arctic natural gas production will account for more than 90% of the country’s production in 2035. China also actively cooperates with neighboring polar countries. The National Development and Reform Commission and the State Oceanic Administration jointly promulgated the “Arctic Ocean Program” for the construction of maritime cooperation along the “Belt and Road”, and incorporated the Arctic region into the strategic vision of the “Belt and Road”. At the same time, it is proposed that China is willing to rely on the development and utilization of the Arctic Passage to build the beautiful vision of the “Ice Silk Road” with other countries [7].

However, in the process of oil and gas exploitation, the harmful substances such as formation cuttings and drilling fluid treatment agents contained in the generated waste drilling fluid will inevitably affect the polar environment [8]. First of all, the drilling process may lead to the accelerated melting of polar glaciers, which in turn affects the global climate. Secondly, the waste liquid, drilling cuttings, and crude oil produced in the drilling process may pollute the polar sea area, causing the pollution of heavy metals and petroleum hydrocarbons [9]. The Exxon Valdez oil tanker grounding accident in 1989 leaked about 110,000 to 120,000 barrels of crude oil. During this period, the economic loss of cleaning up the leaked crude oil and repairing the ecological environment exceeded USD 2 billion. In 2010, the Deepwater Horizon oil well explosion caused about 210 million gallons of leakage. It was one of the largest offshore oil leakage accidents in history, and the total economic loss caused by it reached tens of billions of dollars. These accidents have caused irreversible serious pollution damage to the polar seas. Finally, the drilling process may cause damage to the habitat of polar animals and reduce species diversity. Considering the particularity and vulnerability of the polar environment, it is very important to effectively monitor the risk of environmental pollution and reduce the impact of environmental pollution on the polar region while exploiting oil and gas in the polar region. However, the Arctic region has not yet established a special legal system for environmental protection. Instead, it relies on the globally applicable international convention, the United Nations Convention on the Law of the Sea, and lacks a regulatory and legal framework specific to the Arctic region [10]. At the same time, the convention also leads to a lack of guarantees for its actual implementation in the surrounding Arctic countries, and there may be conflicts [11]. The environmental protection of polar drilling is still in its infancy. This paper aims to study the feasibility of the application of environmental risk identification technology in polar drilling, analyze the current research status of risk identification technology, and look forward to the establishment and development of polar drilling environmental risk identification technology in the future.

This paper mainly studies the technical method of identifying and evaluating the environmental impact risk considering the particularity of polar drilling. The second section of this article describes the challenges encountered. The third section discusses the specific technical methods that can be used in this aspect: the fuzzy comprehensive evaluation method, Bayesian network, cloud model, and neural networks (Figure 2). The main ideas, basic principles, application, and research in the field of risk assessment at home and abroad of these technical methods are discussed. At the same time, the limitations of these methods and ideas for improvement are also discussed for the polar drilling context. Finally, the limitations of the current research are summarized, and the future research in this field is prospected.

2. Environmental Pollution Risk of Polar Drilling and Completion

Drilling and completion activities in the oil and gas industry involve multiple processes that can lead to environmental pollution if not carefully managed. Common risks during these operations stem from the discharge of drilling fluids and cuttings, which may contain heavy metals, hydrocarbons, and other chemical additives harmful to soil and water systems (Figure 3). The handling of produced water—often laden with salts, radionuclides, and organic pollutants—poses a significant challenge, as improper disposal can contaminate freshwater resources. Hydraulic fracturing, a common completion technique, involves the injection of high-pressure fluids that may leak into surrounding groundwater if well integrity is compromised. In addition, air pollution is a concern, with emissions of volatile organic compounds (VOCs), greenhouse gases such as methane, and particulate matter from flaring, venting, and diesel-powered equipment. Waste management during drilling and completion, including the disposal of solid waste and chemical containers, also carries the risk of soil and surface water contamination if handled improperly. Collectively, these pollution sources can lead to ecosystem degradation, health hazards for nearby communities, and long-term environmental damage.

In polar environments, these conventional risks are compounded by the unique challenges of the region. Unlike other areas, polar regions are characterized by permafrost layers that are highly sensitive to temperature changes. The improper handling of drilling fluids can cause permafrost thawing, resulting in ground instability and increased methane release from thawed organic matter. The extreme cold necessitates the use of specialized drilling fluids, many of which may have toxic effects if released into the environment. Additionally, the prolonged ice coverage and limited sunlight in polar regions slow down natural biodegradation processes, leaving ecosystems more vulnerable to contamination from hydrocarbon spills or chemical discharges. Another critical difference is the ecological sensitivity of polar environments. The flora and fauna in these regions are uniquely adapted to harsh conditions, and any disturbance can cause long-term disruptions. Oil spills in icy waters, for example, are notoriously difficult to clean due to ice cover and the lack of effective containment technologies in sub-zero temperatures. Furthermore, the remoteness of polar regions complicates emergency response efforts, making the rapid containment of environmental hazards a significant challenge. Special attention must also be given to air emissions, as polar regions are critical components of the global climate system. The release of black carbon from combustion engines and flaring operations can deposit onto ice and snow surfaces, reducing albedo and accelerating ice melt. Such processes contribute to the amplification of climate change effects, with far-reaching implications for both the polar environment and the global climate.

3. The Challenge of Identifying Environmental Pollution Risks in Polar Drilling and Well Completion

3.1. Hostile Polar Environment

The polar region is extremely cold, and the perennial blizzards and sea ice make the environment very hostile. The annual average sea surface temperature in the Arctic region is only −20~−50 °C, as shown in Figure 4. This extremely low temperature not only poses a challenge to environmental risk monitors, but also poses a challenge to monitoring equipment. Ordinary monitoring equipment cannot be applied in polar regions. Extremely low temperatures in the polar region can affect the normal operation of mechanical equipment, and storms and snowstorms can cause equipment damage or failure, as shown in Figure 5 Drilling equipment may have a series of risk problems, such as the increased brittleness of materials, cracking of accessories, deformation, and sealing failure [13]. At the same time, staff work in harsh environments, and the risk of safety accidents is high [14]. Therefore, the harsh climatic conditions and geographical environment are important challenges for environmental pollution risk identification technology in polar drilling.

3.2. Lack of Monitoring Data

The polar region is geographically remote, far away from densely populated areas, with difficult transportation and high transportation costs. This makes it expensive and difficult to establish and maintain environmental risk monitoring facilities in these areas. And the establishment of monitoring sites requires a lot of manpower, material, and financial resources, so the coverage of the monitoring network is relatively small. The bad meteorological conditions in the polar regions pose a great challenge to data acquisition [15]. Extremely low temperatures, strong wind, and snowfall conditions may affect the normal operation of the sensor [16], resulting in inaccurate data. In addition, extreme storms and ice movement may damage monitoring equipment. Polar regions are also accompanied by seasonal changes: Polar regions experience extreme seasonal changes and polar nights and polar days. Seasonal changes can pose challenges to data collection and environmental monitoring, as conditions in different seasons can lead to data discontinuities. The formation and ablation of sea ice will affect the accessibility of monitoring sites, and the ice layer may affect the stability of data transmission and equipment. This reduces the comparability of monitoring data between different seasons and years, so the tracking and analysis of environmental changes becomes more complicated. Therefore, the environmental pollution risk identification of polar drilling must pay attention to monitoring data collection. Accurate evaluation results must rely on sufficient monitoring data. In the face of this challenge, remote sensing data collection can be carried out [17]. By using the cloud characteristics of massive data storage and resource release of cloud computing platforms, a remote sensing data service system for large-scale applications can be constructed to obtain polar environmental data.

3.3. Lack of Legal System for Polar Environmental Protection

Compared with other regions, the laws and regulations in polar regions are relatively imperfect, and there is a lack of clear environmental protection standards when evaluating the environmental pollution risk of polar drilling [18]. The United Nations Convention on the Law of the Sea, as the legal basis for global application, is currently the main legal framework for the protection of Arctic oil and gas resources and the environment [19]. All countries take peace and cooperation as principles and actively participate in the development of Arctic oil and gas resources in various ways. On the basis of international conventions, non-mandatory legal norms (soft law) exist widely in many fields such as resource management and environmental protection, and have become a practical choice for countries to govern the Arctic. However, the non-binding nature of soft law makes it helpful to resolve conflicts between the interests of states, and also leads to the lack of guarantee for the actual implementation of the Arctic Treaty, which may put states in conflict with each other. The Arctic region has not yet established a special legal system for environmental protection, but relies on internationally applicable conventions and lacks a regulatory and legal framework specific to the Arctic region [20]. Therefore, the development and improvement of standards and regulations for the polar drilling environment is an important challenge for polar drilling environmental risk identification technology.

4. AI-Based Environmental Risk Identification Method

The research on environmental risk assessment started in the United States and originated from health risk assessment [21]. In the 1930s, there were reports on toxic substances, animal impact experiments, and occupational environmental exposure epidemics, indicating that environmental risk assessment research began developing gradually. With the emergence of a large number of environmental accidents caused by industrial pollution around the world, environmental risk problems have attracted the attention of many industrial developed countries represented by the United States, and there are more and more studies on environmental risk assessment and preparedness measures. In the embryonic stage of early environmental risk assessment, risk assessment studies generally focus on health risk analysis, qualitatively indicating that toxic chemicals can endanger the life and health of humans and mammals [22]. From the late 1960s to the 1990s, there was a period of the rapid development of environmental risk assessment research, and some developed countries gradually established risk assessment systems. Since the 1990s, the connotation of environmental risk assessment has been continuously expanded and improved. It is no longer limited to the analysis of health factors of humans and mammals, and risk assessment of the natural environment has also received increasing attention from society as a whole. At present, the commonly used risk assessment methods include the Bayesian network method, fuzzy comprehensive evaluation method, cloud model evaluation method, neural network method, and so on. With the developments of recent years, these methods have made improvements and theoretical improvements on the basis of the original theory, reducing the impact of the original limitations.

4.1. Ant Algorithm

In the field of environmental risk identification, the ant colony algorithm (ACO), as a swarm intelligence optimization algorithm that simulates the foraging behavior of ants in nature, has gradually become an important tool to solve complex environmental problems. In recent years, the ACO has been widely used in pollutant diffusion simulation, environmental monitoring network optimization, pollution source identification, and emergency response scheme optimization. In the simulation of pollutant diffusion, the ant colony algorithm can effectively predict the propagation trajectory of pollutants in the atmosphere, water, and soil by simulating the behavior of ants to find the shortest path, which provides an important basis for the location of pollution sources and the prevention and control of environmental pollution. In terms of environmental monitoring network optimization, the ACO can reasonably plan the layout of monitoring points through optimal path search and resource allocation strategies to improve the efficiency of monitoring networks and reduce operating costs.

The ant colony algorithm has good application prospects for the pollution risk identification of polar drilling and completion environments. In polar regions, due to the extreme climate and complex geological conditions, the pollution problems that may occur during drilling and completion are more harmful to the environment. Therefore, it is particularly important to accurately identify and evaluate potential environmental risks. The ant colony algorithm can optimize the identification path of pollution sources and the layout of monitoring networks based on complex environmental data and uncertain factors, and improve the accuracy and efficiency of pollution risk assessment. In order to better adapt to the special environment of polar drilling and completion, improvements in the ant colony algorithm can include the introduction of weight factors of climate and geological characteristics, and enhance the adaptive ability of the algorithm to cope with the dynamic changes and complexity of polar regions.

Ant Colony Optimization (ACO) performs well in fragmented, sensor-sparse polar terrain because autonomous agents lay pheromone trails without the need for central coordination; however, the classical algorithm is prone to stagnation at local optima. Recent hybrid variants incorporate global search operators—including genetic crossover, simulated-annealing mutations, the sparrow search algorithm, and fuzzy logic sub-populations—to diversify exploration and shorten the convergence time by approximately 20–40%. Controlled ice routing experiments further show that weighting pheromone deposition by surface temperature or albedo gradients guides the swarm toward paths that remain viable under rapid melt conditions, highlighting the benefit of climate-sensitive weighting schemes.

4.2. Bayesian Network

The Bayesian network method was first introduced by computer scientist Judea Pearl in 1986 in his article ‘Fusion, propagation, and structuring in belief networks’ [23]. Then in 1988, he described the theory and method of learning, reasoning, and decision-making of Bayesian networks in his book ‘Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference’ [24]. Bayesian networks can graphically and qualitatively explain the interactions between variables. The structure of Bayesian network-directed graphs can simulate the causal structure in the field of modeling. It can use conditional probability to more accurately describe the relationship between variables and effectively observe the size of the interaction between variables [25]. Moreover, when new data information is obtained, Bayesian networks can easily incorporate it into the network to achieve rapid network updates [26].

Koller and Pfeffer [27] proposed an object-oriented Bayesian network (OOBN) to solve the modeling problem of large-scale complex systems. They draw on the idea of object-oriented programming in software engineering to decompose complex Bayesian networks into multiple reusable sub-networks. These sub-networks are also called classes, and these classes can contain their own sub-networks. The process of determining classes is based on category hierarchy, data type structure, and pattern repeatability. For example, some variables in the system may have recurring properties, forming a particular pattern. By using OOBN, this set of variables can be abstracted into a class and repeated in the model. This method makes modeling more flexible and maintainable and helps to deal with the complexity of large complex systems. A specific example is shown in Figure 6 [28].

The Bayesian network is a decision-making tool based on probability theory and mathematical statistics. It uses graphics to represent the logical relationship between nodes and has a strong application in the research of uncertainty problems such as prediction, diagnosis, and reliability [30]. With the interdisciplinary integration and technological development between disciplines, scholars have tried to apply Bayesian networks to the field of risk assessment, where data is not easy to obtain, and have achieved certain results [31]. We searched the Web of Science database for the relevant literature on the topic of Bayesian networks, and found that from 2012 to 2022, a total of 38,429 articles were published, of which 2531 were related to risk identification. Figure 7 shows the number of papers related to the Bayesian network published each year in the past 10 years. It can be seen that the number of published papers basically shows an upward trend, indicating that researchers are still concerned the Bayesian network method.

Dynamic Bayesian Networks (DBNs) introduce temporal arcs that connect successive time slices, enabling the unified modeling of seasonal pollution pulses, ice melt feedbacks, and cumulative drilling impacts. Recent water quality and ecosystem service studies show that DBNs surpass static models in multi-year forecasting across diverse operational scenarios.

Among them, Zhong Hong and He Sha used the Bayesian network method to calculate the conditional probability of accidents when evaluating the risk factors of human error in coiled tubing operations, and combined it with the fuzzy comprehensive evaluation method to evaluate the risk factors of human error in coiled tubing operations [32]. In the process of studying the quantitative evaluation of the risk of oil and gas pipeline leakage at beaches, Yan Xu and Shuai Yi applied the improved DS-Bayesian network method to evaluate. This method can integrate the opinions of different experts, effectively reduce the subjective influence of expert scoring, and obtain more accurate risk value evaluation [33]. Maroua Abdelhafidh and Mohamed Fourati et al. proposed a new operational risk assessment technique based on dynamic Bayesian networks to identify risk factors and take appropriate initiative to reduce the failure probability of water distribution systems when studying the risk of hydraulic failure in water distribution systems [34]. Christian Enyoghasi and Fazleena Badurdeen proposed a Bayesian network-based quantitative method for risk possibility assessment in continuous product design decision-making. It is applied to the case study of toner box design to verify the feasibility of the method [35]. In order to solve the challenge of obtaining prior data in the risk assessment of submarine pipeline leakage, He Sun and Zhenglong Yang et al. proposed a Pythagorean fuzzy Bayesian network method. This method combines the improved Pythagorean trapezoidal fuzzy Einstein mixed geometric operator with the main target weighting method to obtain the prior probability, and then uses the Bayesian network for inference and analysis to predict the probability of system failure and detect possible vulnerabilities [29]. Xianneng Zha and Huaiwei Su et al. proposed an evaluation method combining the Bayesian network and copula theory for water shortage assessment, and applied it to the Danjiangkou Reservoir, the water source of the Middle Route of the South-to-North Water Diversion Project, and obtained the risk assessment of the project [36].

However, there are also some limitations when using Bayesian networks for risk assessment. First of all, the construction of the Bayesian network model depends on a large amount of data [37]. Secondly, the selection of the appropriate network structure is crucial to the performance of the Bayesian network. However, in risk assessment, the real probability relationship may be complex, and the correct network structure may be difficult to determine, which may lead to the deviation of risk estimation. Due to the harsh environment, polar drilling lacks monitoring data. Using the Bayesian network method in the environmental risk assessment of polar drilling may enable the following research to be carried out: (1) Using satellite remote sensing technology (a remote sensing technology platform [38]) to obtain environmental data of polar drilling, reducing the impact of insufficient data. (2) Combining deep learning to overcome the challenges of traditional Bayesian networks in dealing with large-scale complex data and improve the accuracy of evaluation. (3) Because the environmental risk of polar drilling is dynamic data, it is more meaningful to carry out real-time risk assessment to take corresponding measures immediately. The Bayesian network reflecting dynamic data and time-varying relationships can be studied [39].

Recent studies demonstrate that score-based algorithms—such as Greedy Hill-Climbing and GES guided by BIC or AIC—and meta-heuristic searches (e.g., tabu search and simulated annealing) can recover Bayesian network structures reliably, even in high-dimensional environmental datasets, provided that appropriate regularization is used to curb over-fitting. Because polar risk records are sparse, we augment these data-driven strategies with expert priors: domain specialists impose mandatory or forbidden arcs and supply informative Beta or Dirichlet priors on conditional probabilities, which measurably improve the edge selection precision and convergence speed. For large drilling campaigns, we encapsulate recurring sub-systems—such as multiple well pads or redundant safety barriers—as reusable classes within an object-oriented Bayesian network, thereby cutting the parameter count and making the model logic transparent to non-specialists.

4.3. Cloud Model

The cloud model, proposed by Li Deyi in 1995, is an uncertain transformation model that deals with qualitative concepts and quantitative descriptions [40]. The qualitative concept of the cloud model is characterized by the expected values (Ex), entropy (En), and super entropy (He), which is the overall quantitative expression of the concept. According to the characteristics of Ex, En, and He, cloud droplets are generated to produce different cloud models [41], which have been widely used in the field of risk assessment. The evaluation results of the cloud model are not only subjective, but also objective. The cloud model is based on fuzzy mathematics theory and probability theory [42]. The evaluation results obtained are more intuitive, reliable, and convincing.

The normal cloud model represents a qualitative concept with three digital characteristics—Expectation (Ex), Entropy (En), and Hyper-entropy (He). The forward generator first samples an Entropy (En′) from N(En, He²), and then draws numeric “cloud droplets” x ∼ N(Ex, En′²) to realize the concept, whereas the backward generator inverts the observed x-values via maximum likelihood estimation to recover {Ex, En, He}, enabling bidirectional qualitative–quantitative mapping and the joint modeling of fuzziness and randomness.

Granularity is originally a physical concept used to describe the average measurement of the particle size of matter. However, in the cloud model, it is used as a measure of the information contained in the concept, and analyzes and processes the data in the domain space from different conceptual levels [43]. In the entire granular space structure shown in Figure 8, Layer_i represents the i-th layer, Layer_k represents the finest-dimensional granular layer, and each small dot in Layer_k represents the finest-grained data. Data with similar relationships constitute information granules. Fuzzy sets, rough sets, and quotient space are several common theoretical models of granular computing.

Table 1. Characteristics of different granulation models. (Modified by [44]).

Granular Model	Informofers	Granular structure
Cloud model	Generate cloud according to characteristic parameters	Cloud Concept Tree
Fuzzy set	Fuzzy information granule	If-then principle
Rough set	Equivalent set	Hierarchical rough set
Quotient space	Quotient set	Quotient structure

below lists the different characteristics of granular models, including cloud models.

The cloud model needs to rely on a specific cloud generator algorithm to be implemented, with the forward cloud algorithm (FCG) and backward cloud (BCG) generator. The former is responsible for quantifying concepts, and the latter is responsible for quantifying data. The working principle of the two cloud generators is shown in Figure 9.

Gu Zhiqi and Bian Jianmin et al. proposed a cloud model evaluation method based on combined entropy weight for groundwater quality evaluation and monitoring index optimization in the source area of Changbai Mountain, and used random forest combined with multiple linear regression to construct a water quality index optimization model to determine the key indicators of groundwater sources [45]. In order to evaluate the construction effect of the shipping center in the upper reaches of the Yangtze River in Chongqing, Jiang Jun and Yuan Yiping proposed to establish an evaluation model combining the cloud model and VIKOR methods, and used the CRITIC and ENTROPY methods to obtain the construction effect score of 89.2 points through game combination weighting [46]. Jiawan Liu and Duojin Wang et al. proposed an improved failure mode and effect analysis method based on data envelopment analysis (DEA) and a cloud model in the study of the risk assessment and management of robot-assisted rehabilitation process [47]. Jicun Jiang and Xiaodi Liu et al. proposed a combination of grid clustering and interval rough integrated cloud (IRIC) for evaluation [48] in view of the complex uncertainty in the process of large group decision-making. Yang Zhang and Kejian Shang et al. proposed an improved UFR cloud model evaluation method based on game theory, which combines an analytic hierarchy process (AHP) and entropy weight method (EWM) to effectively evaluate urban flood resistance [49].

The cloud model can realize two-way conversion between quantitative and qualitative, and can overcome the shortcomings of the lack of visibility in the evaluation of a certain concept, so as to realize the effective evaluation of the target, which is widely used in risk assessment. Combined with the current popular big data and artificial intelligence research directions, the cloud model can be further studied in the risk assessment of polar drilling: (1) Aiming at the challenge of the lack of monitoring data in polar drilling risk identification, the cloud model is combined with regression neural [50] and cluster analysis [51] in the data mining [52] method to realize intelligent data mining and supplement monitoring data. (2) Learn from big data technology [53] and artificial intelligence learning solution models to further improve the cloud model theory and improve the accuracy of evaluation. Because cloud theory simultaneously represents randomness and fuzziness, it is well suited to high-latitude datasets in which observations are noisy and sample sizes limited. Empirical risk studies in Arctic shipping and flash flood assessment demonstrate that cloud-based indices preserve more informational content than either traditional fuzzy logic or purely probabilistic metrics. Our next objective is a head-to-head benchmark, using both simulated and in situ polar data, to quantify accuracy, robustness, and computational cost across the cloud, fuzzy, and Bayesian methods—thereby providing an objective foundation for selecting the most appropriate tool.

4.4. Neural Network

The concept of artificial neural networks was first proposed by Warren McCulloch and Walter Pitts in ‘A logical calculus of the ideas immanent in nervous activity’ [54] in 1943, and the mathematical model of artificial neurons was given. Neural network technology has been continuously developed and improved in the past 80 years, and the development process is shown in Figure 10 below. Neural network technology has been evaluated as the most intelligent evaluation and prediction model because of its working principle of imitating biological brain activities, and has been widely used in the field of risk identification [55]. The learning process of the neural network is to use the weight matrix to constantly adapt to external stimuli. Through learning and training, the self-help adjusts the weights of each neuron repeatedly, and the error will tend to be minimal after repeated simulation. The most basic processing unit of the neural network is neurons. Neurons are combined in different ways to form four types: feedback networks, forward networks, mutual combination networks, and hybrid networks [56]. The basic structure of neurons is shown in Figure 11.

Many studies have been carried out on the application of neural networks in risk identification and risk assessment at home and abroad. Xia Yu and Wang Lei proposed an evaluation method based on back propagation neural networks when studying the bidding risk of marine engineering projects. This method first uses the fuzzy analytic hierarchy process to quantify the qualitative problem, and then constructs the evaluation model through the back propagation neural network. The final result has high accuracy [57]. Huang Youtao and Huang Xibing proposed an improved BP neural network method based on the network analytic hierarchy process and sparrow search optimization algorithm when studying the progress risk of prefabricated buildings [58]. Bohan Cao and Qishuai Yin et al. proposed a neural network-based evaluation method when studying the field data analysis and risk assessment of shallow natural gas hazards in industrial deepwater drilling. This method compares the evaluation results of back propagation (BP) neural networks, BP neural networks based on particle swarm optimization (PSO-BP), probabilistic neural networks (PNNs), and fully connected deep neural network. It was found that the fully connected deep neural network has the best effect in identifying shallow gas risks [59]. Ming Chang and Xiangyang Dou et al. proposed a method based on a remote sensing technology platform and optimized a neural network in the study of landslide risk in Afghanistan. This method uses remote sensing technology to obtain the data on Afghan mountains, and then uses the optimized neural network to evaluate the risk of landslides [60].

Although neural networks have been widely used in risk identification and have reached a certain level of intelligence, there are still some challenges in the environmental risk assessment of polar drilling. If the neural network requires a large amount of data during learning and training or if the amount of data is insufficient, the accuracy of evaluation will be greatly reduced. When aiming for the environmental risk identification of polar drilling, the neural network can be further studied: using the big data technology platform, the effective deep neural network model [61,62,63] and learning theory are carried out, the exponential growth knowledge is obtained from the exponential growth data, and the theory of neural network models in risk assessment is further improved.

State-of-the-Art convolutional networks—for example, Mask R-CNN and Vision Transformers trained on Sentinel-1 SAR—push oil spill detection F1-scores above 0.90 by exploiting the multi-polarization texture cues overlooked by classical filters. For atmospheric emissions, stacked or ensemble LSTM models reduce the mean absolute error by 15–30% relative to autoregressive integrated moving average (ARIMA) baselines because they capture strong temporal autocorrelation and exogenous forcings (wind, temperature, and ice cover). When labeled polar imagery is scarce, transfer learning techniques that fine-tune Earth observation backbones on sea ice or permafrost scenes increase classification accuracy by up to ten percentage points while halving the required training data.

Based on the preceding discussions on common risk assessment methods such as the Ant algorithm, Bayesian network, Cloud model, and Neural network, it is evident that each approach possesses unique characteristics and limitations in practical applications. To systematically compare these methods,

Table 2. Comparison of risk assessment techniques.

Number	Risk Assessment Method	Earliest Proposed Time	Advantages of Existence	Disadvantages of Existence
1	Ant algorithm	1992	1. It has a strong global search capability and can effectively avoid local optima. 2. It has strong adaptability and can dynamically adjust search strategies according to environmental changes.	1. It is still prone to getting stuck in local optima. 2. Its algorithm performance is highly dependent on parameter settings, and inappropriate parameters may result in poor performance.
2	Bayesian network	1986	1. When only part of the data is given, the network can be used for probability inference to estimate the probability distribution of other unknown variables.	1. The establishment of the Bayesian network structure requires a lot of data.
3	Cloud model	1995	1. The evaluation results are presented in the form of cloud maps, and the results are more intuitive. 2. It can effectively deal with uncertainty, including fuzziness and randomness.	1. There may be subjective bias when setting weights. 2. Universality is poor and not widely applicable.
4	Neural network	1943	1. It has strong adaptability and can adjust parameters to adapt to different data during training. 2. Excellent performance on large-scale datasets.	1. It is more sensitive to outliers and noise in the input data. 2. For more complex tasks, it takes a lot of time to train.

summarizes their earliest proposed time, inherent advantages, and existing disadvantages, offering a comprehensive overview for better understanding and reference.

5. Combinable Technology

Identifying drilling-related environmental pollution risks in polar regions requires innovative approaches due to harsh climatic conditions and limited pollution data. Remote sensing provides large-scale, continuous monitoring of oil spills, gas emissions, and land disturbances, while transfer learning leverages pre-trained models from other regions to adapt to the unique polar environment, overcoming data scarcity. Big data analytics integrates diverse datasets, such as satellite imagery, meteorological data, and drilling logs, enabling advanced machine learning algorithms to detect spatiotemporal pollution patterns and assess risks. This integrated framework effectively addresses environmental challenges and improves the precision of pollution risk identification in polar regions.

5.1. Remote Sensing Technique

At present, remote sensing technology is mainly used to evaluate drilling and completion environmental pollution in polar regions by monitoring key parameters (such as ice sheet albedo, sea ice cover, black carbon deposition, etc.) to identify pollutant distribution and its impact on the environment, combined with multi-source data fusion and machine learning methods to improve pollution identification accuracy. Optical remote sensing and multi-spectral and SAR technologies have overcome the limitations of polar cloud coverage and polar nights, and are widely used in oil pollution diffusion monitoring, ice and snow albedo change assessment, and climate feedback research on ice and snow melting. It provides a scientific basis for assessing the changes in polar ecosystems. Optical remote sensing and multispectral techniques can be used to detect changes in the albedo of snow and ice caused by pollutants. Thermal infrared remote sensing can be used to monitor temperature anomalies and meltwater distribution. SAR technology is suitable for the long-term monitoring of sea ice dynamics and contaminated deposition because it is not affected by clouds or polar nights. These technical means cooperate with each other to effectively cope with data acquisition challenges under the complex conditions of the polar environment, and lay a solid foundation for the environmental pollution risk assessment of polar drilling and completion.

Qiao Gang, Hao Tong, and other scholars have conducted extensive research on key parameters of polar ice sheets through mapping, remote sensing, and field investigations. Their work includes verifying the air–ground coordination accuracy of the new altimetry satellite for the Antarctic ice sheet, as shown in Figure 12; deploying satellite corner reflectors; validating the internal temperature observation model of the granular snow layer; investigating multi-platform UAV-based sea ice detection and snow–ice environmental conditions; and assessing the quality variations in the Greenland ice sheet. The ice flow velocity measured by GNSS is consistent with the trend of ice flow velocity retrieved by remote sensing. The simulated temperature of the internal temperature model of the snow layer is in good agreement with the measured temperature. There is serious mass loss in the southeastern and western edges of the Greenland ice sheet, while there is mass accumulation in the internal plateau area.

Shusun Li studied the characteristics of polar terrestrial environments (Arctic and Antarctic) and their monitoring methods through remote sensing technology, focusing on the remote sensing monitoring of surface albedo and temperature, surface freeze–thaw status, glacier and other ice bodies’ distribution, tundra soil moisture, vegetation, and forest fires. In addition, the impacts of polar permafrost and snowmelt processes on freshwater release, runoff, and hydrological cycles are analyzed, and it is emphasized that the assessment of inter-annual changes and long-term trends of the polar environment is particularly important in the context of global climate change. The purpose of this study is to reveal the interaction between the cryosphere and the climate system, the geosphere, and the biosphere, and to improve the understanding of polar environmental processes and climate feedback mechanisms.

5.2. Transfer Learning

The application of transfer learning in environmental pollution assessment has gained increasing attention in recent years. The current research primarily focuses on utilizing transfer learning techniques in conjunction with large-scale environmental datasets for pollution prediction and evaluation. Transfer learning enables models to transfer knowledge from a source domain, such as urban or industrial pollution data, to a target domain, such as polar or other specific environmental regions, thereby addressing the challenge of insufficient pollution data. For instance, several studies have employed transfer learning methods to estimate PM2.5 concentrations, demonstrating their adaptability and strong performance across various pollutants.

The current research on the application of transfer learning in environmental evaluation demonstrates its potential to address data scarcity and improve model accuracy across various domains. For instance, Jie Li et al. developed a transfer learning-based model to assess forest fire severity by leveraging remote sensing data from historical wildfire regions. Their results showed that transfer learning effectively projected spectral features between source and target areas, achieving a classification accuracy of 71.20% and a Kappa coefficient of 0.64. Similarly, Jianjun Ni et al. proposed an improved hybrid deep learning model for PM2.5 prediction, incorporating a Maximum Mean Discrepancy (MMD)-based strategy to select optimal source domain stations. The DSTP-DANN model framework based on transfer learning is shown in Figure 13. Their model significantly enhanced prediction accuracy at data-scarce sites and outperformed other State-of-the-Art models. Furthermore, Zhendong Yuan et al. introduced a novel transfer learning framework for hyperlocal air pollution mapping using mobile monitoring data. Their approach demonstrated scalability and adaptability in mapping fine-grained pollution levels in previously unmonitored regions. Collectively, these studies highlight that transfer learning enables the transfer of knowledge from well-studied regions to areas with limited data, offering robust solutions for environmental evaluation tasks, such as pollutant prediction, wildfire severity assessment, and localized pollution mapping. These advancements underscore the promise of transfer learning in addressing critical environmental challenges and bridging data gaps.

To apply transfer learning to environmental pollution identification in polar drilling and completion, it is crucial to address the unique challenges of extreme climates, data scarcity, and complex environmental dynamics. Transfer learning can leverage knowledge from well-studied regions, such as permafrost or high-altitude areas, by employing domain adaptation techniques to align feature distributions between source and polar environments. Integrating polar-specific remote sensing data, such as snow cover and ice melt patterns, enhances model inputs, while incorporating physics-informed priors ensures that predictions reflect the cryosphere’s influence on pollutant transport and accumulation. This tailored approach enables accurate pollution risk assessments in data-scarce polar regions, providing a scalable solution for environmental monitoring in extreme conditions.

5.3. Big Data Technology

The application of big data technology in identifying the environmental pollution risks associated with polar drilling and completion is an emerging area of research, aiming to address the challenges posed by data scarcity and the complexity of polar environments. The extreme climate, seasonal ice and snow cover, and remote geographic location significantly limit traditional monitoring and assessment methods. Big data technologies integrate multi-source data, such as remote sensing imagery, geophysical data, climate records, and historical drilling activity information, to build comprehensive environmental databases for polar regions. For instance, some studies have utilized the fusion of remote sensing data and big data analytics to quantify pollutant distribution and diffusion pathways in polar environments. Simultaneously, real-time sensor networks have been employed to optimize waste management and pollutant discharge assessments in polar drilling operations. Furthermore, the incorporation of machine learning algorithms and advanced big data processing techniques has significantly enhanced the accuracy of pollution detection and prediction models, enabling the identification of potential risk sources and the development of targeted environmental protection strategies. However, the current research faces challenges, such as the difficulty of data acquisition, the complexity of modeling, and the insufficient evaluation of dynamic environmental changes. Addressing these limitations will be crucial for advancing the application of big data technologies in polar drilling and completion pollution risk assessments.

Recent studies highlight the growing role of big data technologies in environmental assessment, demonstrating their potential to address complex environmental challenges through the integration and analysis of multi-source datasets. For example, Xinxue Jinet et al. (2022) [66] propose a big data-driven approach to address environmental pollution control in energy-ecological economic zones. It involves collecting online monitoring data, constructing a data quality model for ecological pollution, and validating the method through simulations. Results show that the proposed approach significantly enhances data quality for online monitoring compared to other methods, demonstrating its effectiveness in supporting robust pollution management in these regions, The construction idea of the environmental protection big data sharing platform is shown in Figure 14.

6. Prospect of Polar Drilling Environmental Risk Identification Technology

Considering the complexity of the environmental risk of drilling in the guide pole, this paper believes that the following two points are worth studying in future related risk identification technology research:

(1)

The realization of real-time monitoring system

Traditional risk assessment methods are usually based on historical data and static models, which are limited in the rapidly changing polar climate and sea conditions. And for sudden major environmental accidents, it is difficult to implement timely response measures in static risk monitoring and risk identification technology. Risk identification research in the future should pay more attention to real-time monitoring and feedback mechanisms, using advanced sensor technology [67], satellite remote sensing data platforms [68,69], and IoT devices [70] to achieve real-time monitoring of environmental parameters and real-time dynamic assessment of environmental risks [71]. This will minimize the impact of potential environmental risks.

(2)

The realization of intelligent monitoring systems

Due to the harsh environment and changeable climate in the polar region, there is inevitably a lack of personnel for environmental risk monitoring and the evaluation of polar drilling. There is a lack of human resources in the polar environment, and further research is needed to realize the intelligence and automation of polar environmental risk assessment. Combining artificial intelligence and machine learning technology [72], the evaluation system can better understand and adapt to complex environmental conditions, while reducing the dependence on human resources and improving the efficiency of environmental risk assessment. Future research should focus on how to integrate technology to form an integrated environmental risk assessment system. This includes the continuous upgrading of real-time monitoring technology [73,74], the construction of big data analysis platforms [75], and the application of artificial intelligence and machine learning algorithms. This integrated system will provide more comprehensive and reliable environmental risk information for polar drilling, more operability and scientific basis for reducing environmental pollution risk, ensure that the environmental risk of polar drilling can be accurately evaluated, and that measures can be taken before key environmental accidents occur to reduce damage and loss.

(3)

Environmental accidents occur to reduce damage and loss

Remote sensing technology can be used to reduce the environmental impact of the process of Arctic oil and gas exploitation. The Arctic Sea area is widely distributed. Using meteorological satellites to monitor the Arctic Sea area in real time can quickly detect possible pollution and reduce the possibility of accidents. The processing of remote sensing data can separate the oil film on the ocean surface from the seawater background, so as to calculate the area of the oil area and the specific oil volume and improve the accuracy of pollutant determination. Remote sensing technology can cover a vast sea area, make up for the shortcomings of traditional monitoring methods, ensure comprehensive monitoring of the entire Arctic Sea area, and minimize the impact of oil pollution on the marine environment. In addition, remote sensing technology can also monitor the status of the ozone layer in the Arctic region, detect and respond to possible cavity problems in real time, and ensure the health and stability of the atmosphere.

7. Conclusions

The polar region has become a focus of world attention due to its great potential for oil and gas resources. However, due to the particularity of the polar environment, its environmental risks cannot be ignored when drilling for oil and gas exploitation. At present, the following challenges are mainly faced in the identification of environmental risks: The polar environment is harsh, the monitoring data is lacking, and the polar environmental protection legal system is lacking. This paper focuses on the current popular risk identification technology: fuzzy comprehensive evaluation methods, Bayesian network methods, cloud models, and neural networks. The feasibility and limitations of their application in polar environmental risk assessment are described, and the prospect of further polar drilling environmental risk is considered. In view of the particularity of polar drilling, the current popular methods have the following common limitations:

(1)

Accurate risk assessment must rely on a large number of monitoring data. Because of its remote geographical location and harsh environmental climate, it is difficult to collect monitoring data. The next step is to use the rocking platform technology satellite to collect data from the polar region to complete the establishment of the evaluation model.

(2)

It is very important to select the appropriate evaluation model for risk assessment, but there are few studies on polar environmental risks, so it is difficult to improve the model and evaluation technology theory. However, with the improvement of computing power, artificial intelligence and machine learning technology are booming. The above evaluation methods can be combined with this technology to learn a large amount of data through computers, so as to improve the evaluation theory and enhance its applicability to the environmental risk assessment of polar drilling. The environmental risk identification technology of polar drilling is very important, but there are still great challenges and a lack of corresponding research. The next step is to carry out more in-depth research and establish a scientific and feasible evaluation system for environmental risks of polar drilling as soon as possible.