The Dual-Core Driving Mechanism of Intelligent Oilfield Development: From Data Perception to Decision-Optimized Ecosystems

Abstract

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems.

1. Introduction

Intelligent oilfield development involves deeply integrating next-generation information technologies with the petroleum industry. As shown in Figure 1, by leveraging advanced sensor technologies, communication systems, computer science, and artificial intelligence across exploration, development, production, transportation, and sales, it enables comprehensive perception, smart analysis, and scientific decision-making in oilfield operations. This method improves resource recovery rates, lowers production costs, safeguards the environment, and enhances safety management.

Amid increasing global energy supply-demand imbalances, traditional extensive oilfield development models struggle to meet modern needs. As easily accessible hydrocarbon resources dwindle, newly discovered fields mainly display the “three highs”—high temperature, high pressure, and high geological stress [1]—making them difficult and risky development targets. At the same time, stricter environmental regulations and societal expectations require higher sustainability standards for energy production. In today’s global economy, the economic benefits from oil revenue often clash with environmental protection. Conversely, energy efficiency and globalization tend to speed up environmental impacts [2]. In this environment, intelligent oilfield development has become a strategic solution. By using advanced information technologies, it reduces development costs and increases hydrocarbon recovery, creating a balance of economic and environmental gains.

Despite the substantial research achievements in addressing these challenges, existing literature reviews largely focus on the isolated application of specific technologies in oilfield development, lacking a systematic and quantitative analysis of the evolutionary logic of knowledge in this field from a global perspective. Furthermore, factors such as the high confidentiality of oil and gas industry data, the high cost of acquiring field data, and the limited scale of publicly available data often lead to research based on limited datasets, further exacerbating the fragmentation of research perspectives. In particular, there is a lack of research that systematically analyzes the knowledge structure of this field using scientometric methods, clearly defining the synergistic evolutionary relationship between the two technological paradigms of “data cognition” and “decision optimization.”

This dual limitation in academic perspective and data availability makes it difficult for researchers to grasp the inherent laws governing the evolution of smart oilfields from single-technology applications to a systematic, full-industry-chain intelligent ecosystem from a macro perspective. Therefore, conducting scientometric research based on citation space analysis not only helps to trace the development of this field, but also, based on limited publicly available data, can quantitatively identify the migration trajectory of research hotspots and emerging frontiers, revealing the coupling relationship between underlying intelligent technologies and complex oilfield conditions. This provides a scientific evolutionary basis for the formation of the “dual-core driven” framework of “data cognition-decision optimization” in the intelligent oilfield decision-making system.

Significant progress has already been made in the development of intelligent oilfields. Internationally, major oil companies such as ExxonMobil, Shell, and BP have established comprehensive digital operation frameworks [3]. Domestically, leading Chinese oil companies—including China National Petroleum Corporation (CNPC), Sinopec, and China National Offshore Oil Corporation (CNOOC)—are actively adopting intelligent oilfield projects. Notably, CNOOC has built an integrated management system that uses artificial intelligence, big data analytics, and diagnostic prediction for monitoring, fault prognosis, and operational optimization [4].

This paper focuses its search on “Intelligent/Smart Development” rather than the broader “Digital Oilfield” to highlight the technological inflection point in the transformation of oilfield development from information-based to intelligent decision-making. Although the sample size is 225 articles, these documents constitute the core citation foundation of current intelligent development research, helping to more clearly reveal the dual-core driving pattern of “AI cognition” and “operations research optimization”.

The objective of this study is to systematically analyze relevant literature in the field of intelligent oilfield development. Using scientometric methodologies, it aims to identify research hotspots, knowledge structures, and developmental trends within this area, thereby providing references and insights for researchers and practitioners. Cluster analysis of core disciplines and keywords shows a strongly interdisciplinary nature in current research. This mainly focuses on applying core AI algorithms such as machine learning and deep learning to achieve high-precision production forecasting and optimize injection-production schemes within complex reservoir environments. This shift from “data-driven” approaches to “intelligent decision-making” has become a technological pathway for improving operational efficiency and maximizing ultimate recovery. The main contributions of this paper are:

(1) Revealing a “dual-core driven” research pattern: Through scientometric analysis, this paper defines for the first time the dual-core knowledge structure of “AI intelligent cognition” and “operations research optimization decision-making” in oilfield development.

(2) Methodological innovation and data refinement: A custom data processing workflow was developed, achieving deep cleaning and standardization of multi-source heterogeneous data, ensuring the high accuracy and scientific rigor of the analysis samples.

(3) Comprehensive system construction and cutting-edge exploration: The paper systematically reviews the entire process technology system from “perception” to “execution,” and deeply explores the engineering application prospects of deep reinforcement learning (DRL) and safety resilience.

Differences between this paper and existing reviews lie in:

(1) Shifting from qualitative description to quantitative scientometrics: Unlike traditional experience summaries, this paper uses co-citation analysis and keyword clustering to transform the field’s hot topics from a fuzzy distribution into a clear networked quantitative structure.

(2) Emphasizing “cognition-decision” synergy and engineering implementation: Moving beyond discussions of single technologies, this paper focuses on demonstrating the necessity of combining AI with mathematical optimization, and proposes solutions to practical engineering bottlenecks such as data silos and model generalization.

2. Materials and Methods

Since intelligent oilfield development requires deep interdisciplinary collaboration across petroleum engineering, computer science, geology, and operations research, traditional qualitative literature review methods often struggle to objectively and comprehensively capture the field’s rapidly changing dynamics and complex knowledge structure. Therefore, this study develops a rigorous scientometric analysis framework to uncover the academic trajectories and technological trends hidden within large collections of literature through data mining. Unlike conventional literature analysis, this study not only relies on popular visualization software but also innovatively introduces a custom data processing tool chain based on the Dart programming language. The methodological innovation involves using programming scripts to thoroughly clean, tag, and standardize raw, multi-source, heterogeneous data. This approach effectively overcomes the limitations of traditional manual screening, ensuring high accuracy and consistency in subsequent analysis samples. Building on this foundation, combined with the complex network clustering algorithms of VOSviewer 1.6.20 software, it achieves a systematic breakdown of the field’s hotspots from a “point distribution” to a “network structure” perspective.

The research technical approach is shown in Figure 2. The overall architectural design of this study follows a logically rigorous four-stage process: data acquisition, preprocessing, scientometric modeling, and structural visualization. This methodological framework not only ensures the reproducibility of research findings but also provides a solid theoretical basis for analyzing the “dual-core” transition of intelligent oilfield development from “data perception” to “decision optimization.”

Stage One: Data Acquisition and Retrieval Strategy. The data source for this study was the Web of Science (WOS) Core Collection database. To ensure both high recall and precision in the retrieval results, a rigorous search strategy was developed. Specifically, multiple retrieval fields—including Topic, Title, Abstract, and Author Keywords—were used together. The search query was built using combinations of key terms, such as TS = (“Smart Oilfield” OR “Intelligent Oilfield”), OR TI = (“Smart Oilfield”), OR AB = (“Smart Oilfield”), OR AK = (“Smart Oilfield”). Additionally, to ensure the research sample’s academic quality and international comparability, document types were limited to Articles and Bibliographies, and the language was strictly set to English.

Stage Two: Screening & Custom Pre-processing Data cleansing is an essential step for ensuring accurate quantitative analysis. As shown in the central “funnel” and “gear” modules in Figure 2, this study first performed manual screening and removal of duplicates from the initial literature search, excluding news reports, conference abstracts, and non-academic texts. To resolve issues such as inconsistent formatting and missing data, the Dart tool chain was used for detailed pre-processing. Instead of traditional direct import analysis, a custom Dart script was created to clean, standardize fields, and conduct statistical pre-processing on the raw bibliographic data downloaded in Excel format. This unique “Dart-factor” workflow effectively addressed compatibility issues across multi-source, heterogeneous data, ultimately identifying 225 high-quality core publications as the research sample.

Stage Three: Scientometric Analysis and Modeling was imported into VOSviewer software for quantitative analysis. As shown in Figure 2, this phase mainly conducted three-dimensional analyses: (1) Citation Analysis to map core academic trajectories; (2) Co-citation Analysis to reveal knowledge foundations and scholarly transmission; (3) Keyword Clustering to identify evolutionary patterns of research hotspots. By constructing complex network matrices, the strength of connections between different academic nodes was quantified. VOSViewer’s clustering algorithm is based on association strength, selecting high-frequency keywords in the text for cluster analysis to reflect the research themes of the discipline. We conducted sensitivity tests by adjusting the keyword co-occurrence frequency threshold (from 5 to 15 times), and the results showed that the core “dual-core” topology remained highly consistent, eliminating the interference of data noise.

Stage Four: Outcome Visualization and Structural Revelation presents the study’s final results, as shown on the right side of Figure 2, aiming to uncover the underlying knowledge structure within the domain. Through the development of visual scientific maps, this research clearly identifies the current “dual-core propulsion” characteristics of intelligent oilfield development: namely, an “Artificial Intelligence and Deep Learning” cognitive cluster (depicted in red) and an “Operational Optimization and Predictive Modeling” production decision cluster (depicted in green). These two clusters are interconnected, collectively forming the current research ecosystem for intelligent oilfield development and providing strong data support for subsequent outcome discussions.

This paper uses the Dart toolchain for citation analysis, such as statistical literature sources including country and discipline. These tags can be obtained from complete record files exported from WOS. Therefore, the Dart toolchain is only used for simple statistical visualization, while the more intelligent clustering work is handled by VOSViewer. VOSViewer’s clustering algorithm is based on association strength, selecting high-frequency keywords in the text for cluster analysis to reflect the research themes of the discipline. When using it, it should be combined with data visualization methods for comprehensive analysis. When adjusting necessary parameters (other non-essential parameters such as font and node style), the Full counting parameter was chosen because of the small dataset and the desire to highlight high-frequency words and hot topics.

The reasons for choosing Dart as the toolchain in this paper are:

(1) Dart’s Flutter UI provides beautiful, academic-grade visualization capabilities and supports rich interactive operations, making it very suitable for scientific visualization experiments and prototype development;

(2) Dart + Flutter makes it easy to build streamlined visualization tools, facilitating the rapid transformation of research results into operational software;

(3) The core source code and interactive software source code related to this paper have been open-sourced, facilitating reproduction and secondary development: https://github.com/PythonnotJava/flutter_wordcloud/tree/main/wos, accessed on 1 January 2026.

3. Results and Discussion

3.1. Research Citation Analysis

Through visualization, citations within the field of intelligent oilfield development were analyzed, encompassing publication year, publisher, discipline, research domain, and journal distribution, thereby revealing academic trends and knowledge structures.

Statistics from January 2025 to 10 January 2026 show a significant growth trend in the relevant literature on intelligent oilfield development in the database, as shown in Figure 3. This trend highlights the accelerated penetration and widespread application of cutting-edge technologies such as artificial intelligence, big data, and optimized scheduling in oilfield development. Some studies have pointed out that this technological integration is driving profound changes in oilfield development models [5,6]. At the same time, this also indicates that with the rapid development of new-generation intelligent technologies such as AI big data models, this interdisciplinary field will continue to prosper in the future and usher in deeper levels of innovative breakthroughs, showing broad academic and industrial prospects.

The publication distribution for intelligent oilfield development is shown in the word cloud of Figure 4. Word cloud diagrams utilize a spiral algorithm to place core high-frequency words and these cutting-edge words with small weights but significant meanings together under the same visual dimension (considering that bar charts and Pareto charts may not be effective due to the extremely small weights of some words and the wide variety of word classes involved), thus avoiding information fragmentation. It is a combination of “visual panorama + precise data measurement”. Figure 4a illustrates the distribution of primary publishers, with Elsevier, MDPI, Springer, Wiley, and ACS leading, indicating that research in this area is highly concentrated within the mainstream international academic publishing system. Figure 4b displays the research domains, mainly focused on Engineering, Energy & Fuels, and Petroleum Engineering, while also including Chemistry, Materials Science, Geosciences, Physics, and Information Systems, reflecting a strong engineering focus and multidisciplinary integration. Figure 4c highlights journal distribution, with the Journal of Petroleum Science and Engineering, Energy & Fuels, and Petroleum Exploration and Development serving as key publication platforms, balancing engineering, energy, and interdisciplinary applications. Figure 4d shows disciplinary distribution, with engineering and energy being most prominent, developing synergistically alongside chemistry, materials science, physics, and computer science. This demonstrates the comprehensive and systematic nature of intelligent oilfield development.

Within the interdisciplinary field of intelligent oilfield development, research is gradually shifting from isolated technological applications toward deep integration of multiple technologies and systemic coordination. As oil and gas field development targets become more complex, production processes grow smarter, and safety and efficiency demands increase, traditional information technology and automation methods struggle to meet needs for high precision, real-time responsiveness, and overall optimization. As a result, emerging technologies such as unmanned aerial vehicles, artificial intelligence, big data, the Internet of Things, digital twins, and intelligent robotics are converging across disciplines to create an intelligent oilfield technology system called “perception-cognition-decision-execution.” Process

Table 1. Representative Cross-Disciplinary Core Technologies in Oilfield Development.

Interdisciplinary/Technological Integration	Primary Applications	Technical Characteristics and Integration Directions	Performance Indicators or Advantages
Unmanned Aerial Vehicle (UAV)	Oilfield inspection, pipeline and wellsite monitoring, leak detection, emergency response [7]	Integrated with computer vision, remote sensing, and GIS to achieve low-cost, high-frequency, automated sensing	Unmanned aerial vehicle (UAV) remote sensing typically provides spatial resolution from centimeters to sub-meters, significantly higher than traditional satellite remote sensing (usually meters to tens of meters) and some aerial remote sensing (generally decimeters). Therefore, in applications such as oil and gas infrastructure inspection, pipeline monitoring, and environmental monitoring, UAV platforms can achieve higher precision spatial information acquisition and more flexible and rapid data collection [8].
Artificial Intelligence and Big Data	Intelligent interpretation of seismic data, well logging, production, and equipment data; production forecasting, reservoir characterization, intelligent regulation [9]	Driven by machine learning and deep learning to support decision analysis and forecasting	Fault detection accuracy exceeds 90%, while manual interpretation typically achieves around 70–80%. AI can complete this process in hours or even days. Shell uses AI to reduce fault interpretation time by approximately 30%, BP uses AI to reduce seismic acquisition costs by approximately 20%, and ExxonMobil’s AI model achieves reservoir prediction accuracy of approximately 85% [4].
Internet of Things and Intelligent Sensing	Real-time monitoring of wellbore-surface-pipeline networks [10], a decentralized approach enabling secure transmission of oilfield sensor data [11]	Integration of sensor networks, edge computing, decentralization, and wireless communication to achieve end-to-end perception	Research has shown that the Internet of Things (IoT) and intelligent sensing technologies can enable real-time monitoring and data management of oil and gas production equipment. For example, one study constructed an IoT-based prototype oilfield monitoring system, using Raspberry Pi and multiple sensors to collect equipment operating parameters and combining this with blockchain for secure data storage. Experiments validated the system based on over 15 million drilling data records, demonstrating that it maintains good scalability even with increasing data volume and can significantly reduce communication and computing resource consumption through batch data processing, thus supporting large-scale real-time monitoring and operation management of oilfields [12].
Digital Twin Technology and Intelligent Decision-Making	Optimizing oilfield operations and managing risks [13]	Coupling physical models with data-driven models to support intelligent decision-making, simulation-based forecasting	An open-source paper from MIT demonstrates that digital twin technology, by constructing virtual models of oil and gas production systems and combining them with real-time data and predictive analytics, enables real-time monitoring, prediction, and optimization of oilfield operations. The research proposes a series of metrics for evaluating the performance of digital twin systems, including mean time between failures (MTBF), system Fidelity, latency, and lifecycle cost and net present value (NPV). Simulation results show that applying digital twin technology to offshore deepwater production facilities can improve facility availability by predicting equipment failures in advance and reducing downtime, resulting in a net present value increase of approximately $211 million over a 27-year lifecycle, thereby significantly enhancing oilfield operation optimization and risk management capabilities [14].
Robotics and automated equipment	Operating in high-risk, high-temperature, and high-pressure environments, extending to deep-sea oilfield extraction [15]	Replacing manual labor to achieve safe, efficient, and automated operations	Inspection robots in the oil and gas industry demonstrate significant advantages in safety and operational efficiency. Studies show that deploying robotic inspection systems can reduce the number of safety incidents in oil and gas facilities by approximately 60%, primarily because robots can replace human labor in hazardous environments such as high temperatures, high pressures, and confined spaces. Furthermore, the oil and gas industry suffers approximately $49 billion annually in losses due to unplanned downtime caused by equipment failures, and robots can significantly reduce downtime risk through continuous monitoring and predictive maintenance. Currently, a single robotic inspection system costs approximately $500,000 to $2 million, and its design must withstand extreme conditions, such as temperatures ranging from −40 °C to 93 °C and high pressure environments reaching up to approximately 15,000 PSI, to meet the inspection needs of complex oilfield facilities [16].

summarizes key interdisciplinary technology trends within oilfield development and their main application features.

An analysis of the spatial distribution of scholarly publications in the global intelligent oilfield development sector (see Figure 5) shows a clear concentration of research output in the world’s main hydrocarbon-producing nations. The data demonstrate that China, the United States, Russia, Canada, Brazil, and certain Middle Eastern oil-producing countries are the main contributors to academic discussions in this field. This pattern highlights an industry trait driven by resource endowment, which influences research investment.

China leads in publication volume, propelled by its strategic national initiatives in smart oilfield infrastructure, exemplified by advanced offshore applications that integrate monitoring, sensing, and robotics technologies [17]. Traditional oil-producing countries such as the United States, Russia, and Saudi Arabia follow closely. For example, Saudi Aramco advances smart oilfield concepts through multiple projects, incorporating data into upstream operations like drilling, well completion, production management, and reservoir management. Real-time monitoring and remote operations, for instance, improve drilling efficiency, boost production productivity, and increase strategic flexibility [18]. This closely linked geographical distribution suggests that the demand for oil exploration and development is the main driver behind the adoption of cutting-edge technologies such as artificial intelligence and big data in the oil and gas industry. The level of smart oilfield research positively correlates with a nation’s strategic energy stance and resource reserves [19].

3.2. Core Hotspot Analysis

VOSviewer’s clustering algorithm is based on association strength, selecting high-frequency keywords in the text for cluster analysis to reflect the research themes of the discipline. When using it, it should be combined with data visualization methods for comprehensive analysis. When adjusting necessary parameters (other non-essential parameters such as font and node style), since it is a small dataset and we want to highlight high-frequency words and hot topics, we chose the Full counting parameter. The results are shown in

Table 2. VOSViewer Clustering Information.

Cluster ID Keywords Average Publication Year (Approx.)	Cluster ID Keywords Average Publication Year (Approx.)	Cluster ID Keywords Average Publication Year (Approx.)
C1	artificial intelligence	2023
C1	deep learning	2024
C1	machine learning	2022
C1	performance	2021
C1	enhanced oil recovery	2017
C2	model	2020
C2	prediction	2019
C2	optimization	2018

.

Through co-citation analysis and keyword clustering of literature in the intelligent oilfield development domain, this study identifies a clear dual-core structure in the field’s main research hotspots. As shown in Figure 6, the research hotspots are mainly split into two large clusters: the left cluster, focused on artificial intelligence technologies for intelligent decision-making (red area), and the right cluster, centered on production optimization through operational research and mathematical programming (green area). This pattern is further confirmed by the heat distribution map in Figure 7. The dual-core driven research approach indicates a shift in intelligent oilfield development from traditional experience-based methods toward a combined “AI-enabled + mathematical optimization” dual-driven model, capturing two key areas of technological innovation: first, using artificial intelligence techniques for smart understanding and prediction of complex reservoir systems; second, applying operational research and optimization strategies to support scientifically based decision-making and improve operational efficiency in oilfield production systems.

From the evolution of the keyword network diagram and the bibliographic literature, between 2015 and 2018, the research focus was highly concentrated on “enhanced oil recovery,” “predication,” and “optimization,” reflecting that this stage focused on using early heuristic algorithms to solve the bottleneck of single physical mining. Between 2019 and 2022, the keywords shifted significantly to “machine learning,” “performance,” and “model,” marking the industry’s paradigm shift from localized tool-based approaches to a big data-driven cognitive model. Between 2023 and 2025, as “deep learning” and “artificial intelligence” became high-frequency core terms, the research finally established a “dual-core driven” research pattern with the synergy of “intelligent cognition” and “operational decision-making” as the core.

3.2.1. Hotspots in Artificial Intelligence Technology Applications

The red cluster on the left side of the diagram shows that artificial intelligence-related technologies are the main focus of research in intelligent oilfield development. This cluster revolves around key nodes such as “artificial intelligence,” “machine learning,” and “deep learning,” which are closely linked to application goals like “performance” and “enhanced oil recovery” (EOR). The appearance of this focus area marks a major shift in oilfield development from traditional physics-based methods to data-driven intelligent techniques.

As a core technology within this group, machine learning demonstrates strong potential for applications in intelligent oilfield development. Its primary use cases include reservoir parameter prediction [20], production dynamics analysis [21], and equipment fault diagnosis [22]. Compared to traditional numerical simulation methods, machine learning algorithms can autonomously learn complex nonlinear relationships from historical production data without requiring precise physical equations. This approach offers clear advantages when handling high-dimensional, multi-source heterogeneous data. In evaluating oilfield development value, reservoir condition uncertainty is the main factor, accounting for about 80% of the variance in field worth. By employing machine learning models (such as decision trees and random forests) to predict and reduce reservoir parameter uncertainty, the accuracy and reliability of economic assessments can be significantly enhanced [23].

As a vital branch of machine learning, deep learning shows even greater capabilities in intelligent oilfield development. Deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) have been widely used for complex tasks, including seismic data interpretation [24], well log curve recognition [25], and production time series forecasting [26]. Compared to traditional machine learning methods, deep learning can automatically extract deep features from data without manual feature engineering, making it especially effective when processing unstructured data such as images and sequences. For example, in the PSO-CNN-LSTM hybrid neural network model, the study used logging data from 8 oil wells in a gas reservoir in Kansas. After preprocessing, a total of 3232 samples were obtained. The study focused on 501 sets of data from Well 1 with depths between 2573.5 and 2841.5 m, and performed single-well prediction analysis at a speed far exceeding that of manual methods [27].

The co-citation network shows that “enhanced oil recovery” is closely connected to the artificial intelligence technology cluster, highlighting AI’s broad use within the EOR field. Traditional recovery methods depend mainly on physical experiments and numerical simulations, which are often slow and expensive. Using artificial intelligence techniques offers new ways to quickly evaluate and improve EOR methods. Machine learning algorithms can efficiently predict the success and expected results of different displacement techniques (such as chemical flooding, gas flooding, thermal recovery, etc.) based on specific reservoir conditions [28]. Additionally, deep reinforcement learning methods are used for decision-making tasks like optimizing injection-production well networks and choosing the best amount of displacement agents. By working with reservoir simulators, these techniques can automatically find the most effective EOR strategies [29].

In high-risk, capital-intensive industries like oilfields, any misjudgment has significant economic consequences. Therefore, the reliability, robustness, and interpretability of AI models-collectively called “Performance”—are essential. Current research emphasizes not only predictive accuracy but also a model’s ability to generalize across diverse reservoir conditions, handle anomalous data, and maintain transparency in decision-making processes. To improve AI model reliability, researchers employ several technical approaches: first, enhancing model generalization through techniques like ensemble learning and transfer learning [30]; second, implementing uncertainty quantification methods to provide confidence intervals for predictions [31]; and third, adopting explainable AI technologies-such as SHAP value analysis and attention mechanism visualization—to help engineers understand the model’s decision logic [32].

Table 3. Case Studies of Artificial Intelligence Technology Applications in Oilfield Development.

Process	Technology	Specific Applications
Exploration	Machine learning + deep learning	Japanese oil company INPEX has partnered with a technology company to utilize machine learning models for fault identification and reservoir structure interpretation in oil and gas exploration projects in Southeast Asia. Researchers manually labeled only a small number of faults (approximately 4% of the total seismic data volume), then used machine learning models to automatically infer fault structures in the remaining 3D seismic data. This reduced structure interpretation time by about 80% and enabled rapid identification of potential oil and gas traps.
Drilling	Deep learning combined with time series models, anomaly detection algorithms, etc.	The model automatically recommends optimal drilling trajectories and well locations, while also predicting equipment failures. In actual operation, ADNOC achieved a 23% increase in recovery rate, reduced annual drilling costs by approximately $480 million, and increased equipment utilization by 34%.
Well completion	Machine learning model + real-time data control system	The model analyzes fracturing pressure, injection rate, and other parameters in real time, resulting in a 78% reduction in fracturing operation time and premature termination of fracturing operations in Halliburton and its partners’ practices.
Gathering and Transportation	Researchers are using pipeline operation data (such as pressure, temperature, and flow rate) to build machine learning models for real-time anomaly monitoring of gathering and transportation pipelines.	Researchers have developed an intelligent leak detection system. This system can identify abnormal pipeline conditions and determine whether a leak has occurred through real-time data. The model achieves a leak detection accuracy of approximately 97.4%, is highly automated, and reduces the need for manual inspections.

shows some case studies of artificial intelligence technology applications in oilfield development.

3.2.2. Hotspots in Operations Research and Optimization Applications

The green cluster on the right side of the diagram highlights the second major research focus in intelligent oilfield development: the use of operations research and optimization techniques. Centered around key nodes such as “model,” “optimization,” and “prediction,” this cluster emphasizes the crucial role of system optimization principles based on mathematical modeling in oilfield production management. Compared to the AI cluster on the left, the operations research and optimization cluster focuses more on structured mathematical modeling and solution algorithms, aiming for optimal or near-optimal solutions within specific constraints.

The co-citation network indicates that “predication” is closely linked with “optimization” and “model,” emphasizing the strong connection between prediction and optimization. In intelligent oilfield development, accurate prediction is vital for making well-informed decisions, while optimization serves as the tool to achieve predictive objectives. These two elements work together, forming a continuous cycle of “prediction-optimization-execution-feedback.”

From an “intelligent” perspective, the “intelligence” embodied by the operations optimization cluster goes beyond individual models or algorithms. Instead, it facilitates centralized decision-making and scheduling across wells, blocks, and even systems through cloud-based platforms. In intelligent oilfield development, production data, reservoir models, surface engineering constraints, and economic indicators are usually consolidated onto cloud-based decision platforms. Centralized or distributed optimization models then conduct a comprehensive analysis of this multi-source information, enabling holistic, coordinated optimization of injection-production structures, output allocation, energy consumption control, and equipment operational status [33].

Regarding specific optimization approaches, such as intelligent operations optimization, it typically features characteristics of multi-algorithm fusion and hierarchical coordination. On one hand, meta-heuristic algorithms (such as genetic algorithms, particle swarm optimization, and ant colony optimization) are widely used to solve high-dimensional, non-linear, non-convex, and complexly constrained oilfield production optimization problems, improving the ability to avoid qualitative and local optima [34]. On the other hand, reinforcement learning methods enable adaptive learning of dynamic injection-production strategies and long-term benefits through continuous interaction with the reservoir-production system, making them suitable for time-series decision-making [35].

3.2.3. Could Deep Reinforcement Learning Be the Next Frontier?

Although Deep Reinforcement Learning (DRL) has appeared in 225 related papers, its overall co-occurrence strength remains relatively low compared to other research hotspots. In automatic clustering analysis based on keyword co-occurrence, independent cluster structures are only formed when keywords reach a certain frequency threshold. Since the application of DRL in the oilfield is still in its early stages of development, its keywords have not yet reached the threshold for independent clustering. Although DRL has not yet formed independent nodes in the macro-knowledge graph, its impact on intelligent oilfield development should not be underestimated.

Deep Reinforcement Learning (DRL), a key branch of artificial intelligence, combines deep neural networks with reinforcement learning. Through agents continuously learning optimal strategies via trial and error in various environments, it has made significant advances in gaming and robotic control. In intelligent oilfield development, DRL’s potential applications mainly address dynamic, complex, and uncertain decision-making challenges, such as optimizing injection-production schemes, planning drilling paths, scheduling equipment, and allocating resources. Unlike traditional operations research optimization methods, DRL does not depend on explicit mathematical models. Instead, it learns by interacting with simulated environments, adapting to real-time reservoir conditions and operational constraints to maximize long-term value and enable closed-loop autonomous coordination (as shown in Figure 8).

Current research shows that DRL applications in oilfield development are advancing quickly. For example, in constrained field development optimization for two-phase subsurface flow, DRL agents can create development plans for immediate optimization through millions of fluid simulations, providing instant optimized plans that significantly outperform traditional pattern recognition agents [36]. Additionally, DRL has been used in generalized field development optimization, managing scenarios with different reservoir descriptions, operational constraints, and economic conditions while lowering computational costs and improving decision-making efficiency. These uses not only boost recovery rates but also reduce operational risks, making them especially suitable for high-temperature, high-pressure, and high-resistance (‘three-high’) oilfields [37].

Despite research showing the significant potential of deep reinforcement learning in intelligent oilfield development, its large-scale engineering application faces several key challenges. One issue is that DRL methods generally depend on high-fidelity numerical simulations or digital twin environments for offline training, leading to high training costs, low sample efficiency, and a strong reliance on the accuracy of simulations [38]. Another challenge is that the interpretability and stability of policy networks remain limited. In oil and gas production scenarios with strict safety requirements, fully autonomous decision-making calls for careful validation [39]. Additionally, real-world oil fields often involve complex factors like multiscale coupling, multi-physics interactions, and sudden operational changes, which increase the demands on DRL’s ability to generalize and remain robust in real environments.

Furthermore, while deep reinforcement learning agents demonstrate excellent optimization capabilities after millions of fluid simulations, this training process is often accompanied by significant computational overhead and high hardware infrastructure requirements. In engineering practice, the scale of this computational load typically depends on the complexity of the specific task and the size of the reservoir. For ultra-large-scale, highly nonlinear complex oil and gas reservoir problems, computational demands can grow exponentially, usually requiring support from high-performance computing clusters (HPC) or cloud computing platforms. However, in small to medium-sized mature oilfields or simplified physical model scenarios, by introducing reduced-order models (ROMs) or parallel computing strategies, near real-time decision-making can be achieved under relatively controllable hardware costs. Therefore, the construction of future smart oilfield systems needs to achieve a reasonable balance between “model accuracy” and “computational scalability.”

Notably, with advances in computational power, parallel simulation techniques, and the maturing application of digital twins in oilfield lifecycle management, the boundaries of DRL application are continually expanding. Current research trends indicate that DRL is progressively forming a complementary relationship with traditional operations research optimization and meta-heuristic algorithms. For instance, DRL is employed for policy learning in high-dimensional decision spaces, subsequently combined with methods such as particle swarm optimization and genetic algorithms to conduct refined searches for local solutions, thereby effectively addressing non-convex and strongly constrained optimization problems [40]. Concurrently, the closed-loop architecture based on digital twins provides DRL, with a sustainably updatable training environment, enabling online calibration and adaptive evolution capabilities [41].

Overall, deep reinforcement learning is unlikely to completely replace traditional reservoir engineering and optimization techniques in the near term. However, it offers unique strengths in complex decision modeling, real-time optimization, and cross-scenario generalization. As the collaborative framework of “AI + digital twin + operations research optimization” continues to develop, DRL is positioned to shift from a supporting decision-making tool to a central technology in intelligent oilfield development systems, showing strong potential to become a leading focus in research and application in the future.

4. Conclusions and Outlook

4.1. Security Risk Management and Resilience Building in the Context of Intelligentization Cannot Be Ignored

In the evolution of intelligent oilfield development toward a fully integrated value-chain intelligent ecosystem, safety management has progressed from traditional on-site physical protection to a comprehensive framework that includes cybersecurity, model decision security, and environmental compliance. Because the Intelligent Oilfields rely heavily on the Internet of Things (IoT) and intelligent sensing technologies for real-time monitoring across the wellbore, surface, and pipeline network, this widespread connectivity significantly expands the system’s attack surface [42]. Sensor networks deployed in the field and extreme operational environments face risks such as data leakage and command tampering. As a result, researchers are exploring decentralized blockchain technologies to ensure the integrity and security of oilfield data transmission [13]. Additionally, as artificial intelligence becomes deeply embedded in production decision-making, the robustness and explainability of AI models have become key performance indicators [43]. This is especially critical under the extreme “three-high” conditions-high temperature, high pressure, and high ground stress-where AI models must demonstrate strong uncertainty quantification capabilities to prevent decision errors caused by data drift, which could lead to significant economic losses or safety incidents. While applying blockchain to sensor data acquisition can ensure data integrity and reliable transmission, its inherent consensus mechanism and distributed synchronization can lead to significant response delays under extreme high-temperature and high-pressure environments. This delay can disrupt the millisecond-level timeliness required by real-time closed-loop control systems and may cause network congestion or decision lag when processing high-frequency sampled data. Therefore, the current trend is to combine blockchain with edge computing for rapid local anomaly identification to alleviate the computational load on the blockchain and ensure the system’s resilience under extreme conditions. Additional mainstream application scenarios are shown in

Table 4. Representative Technologies for Safety Risk Management and System Resilience Construction in Intelligent Oilfields.

Safety Risk Dimensions	Representative Technology	Primary Application Scenarios	Risk Prevention and Resilience
Cyberspace Security	Blockchain and Distributed Ledger Technology	Oilfield Sensor Data Acquisition and Production Data Sharing	Leveraging decentralized ledgers and immutability to ensure data integrity, traceability, and transmission reliability
Data and Communications Security	Blockchain and Distributed Ledger Technology	Oilfield Sensor Data Acquisition and Production Data Sharing [13]	Leveraging decentralized ledgers and immutability to ensure data integrity, traceability, and transmission reliability
Device and Edge Security	Edge Computing and Local Anomaly Detection	Remote well sites and unmanned stations, such as the Huawei Smart Well Site, a collaboration between Changqing Oilfield and Huawei	Enabling rapid anomaly detection and local decision-making at the edge to reduce reliance on central systems and enhance operational resilience under extreme conditions
Model decision security	Robust Optimization and Uncertainty Quantification (UQ)	Production Control, Injection-Production Optimization, Risk Early Warning [44]	Enhance AI decision stability under distribution drift and extreme conditions by explicitly modeling data and environmental uncertainties
Model interpretability	Explainable Artificial Intelligence (XAI)	Production Decision Support, Anomaly Diagnosis [45]	Improve model decision transparency to strengthen engineers’ understanding and trust in AI decisions, reducing misuse risks
System-Level Risk Simulation	Integration of Digital Twins and Intelligent Decision-Making	Accident drills and assessments of extreme operating conditions, such as IBM’s Digital Twin for Oil & Gas platform.	Perform risk scenario simulations and strategy assessments in virtual environments.
Environmental and Compliance Safety	Multi-source Perception and Intelligent Monitoring	Emissions monitoring and wellsite environmental protection, such as the Honeywell Emissions Management Suite monitoring platform	Monitor environmental risks in real time to support compliance decisions and enhance system adaptability to external constraint changes

below.

4.2. Research Summary

This paper systematically reviews the current state and technological hotspots of intelligent oilfield development research based on bibliometric analysis. Results indicate a significant growth trend in intelligent oilfield research over the past decade, with scientific achievements predominantly concentrated in major oil-producing nations such as China, the United States, and Saudi Arabia, reflecting a strong correlation between resource endowment and research investment. Knowledge structure analysis indicates that intelligent oilfields have formed a dual-core research framework:“ AI-driven intelligent cognition” and “operational optimization-driven production decision-making ”. Machine learning and deep learning technologies have been extensively applied in reservoir characterization, production forecasting, equipment condition diagnosis, and enhanced recovery. Meanwhile, operations research optimization methods play a pivotal role in injection-production structure optimization, production scheduling, and economic trade-offs. As an emerging technology, deep reinforcement learning demonstrates potential in dynamic decision-making and complex optimization. Although its engineering application still faces challenges such as interpretability and training costs, its development prospects remain promising. Furthermore, the increased link strength between edge computing and blockchain nodes in the metering network indicates that security has shifted from “external safeguards” to “intrinsic architectures.” In the future, data integrity, model robustness, and network security protocols should be deeply integrated as necessary prerequisites for achieving “dual-core collaboration,” rather than as isolated technical modules.

The technological evolution directions focused on in this paper—such as the transition from descriptive models to autonomous collaborative deep reinforcement learning (DRL) and blockchain-enabled security architectures—are widely recognized as important trends in the digital transformation of the global oil industry. Based on our observations, related research in China (e.g., CNPC, Sinopec) and Russia (e.g., Rosneft) is also making continuous progress in these key areas. Therefore, although the sources of literature are somewhat geographically limited, the technological paths summarized in this paper still possess strong industry universality and forward-looking perspective.

4.3. Research Outlook

Several technical challenges remain in intelligent oilfield development: (1) severe data silos exist, with data from different departments and levels proving difficult to share and integrate effectively, lacking unified data standards and interface specifications; (2) insufficient indigenous innovation capabilities for key technologies, with certain core algorithms and software platforms still reliant on imports; (3) immature techniques for fusing multi-source heterogeneous data, requiring enhanced collaborative analysis capabilities for diverse datasets such as seismic, logging, and production data; (4) models exhibit insufficient generalization capabilities and robustness, with algorithm adaptability and reliability needing enhancement when confronting complex and variable reservoir conditions; (5) bottlenecks persist in the engineering application of real-time optimization and control technologies, as closed-loop feedback mechanisms linking intelligent decision-making to field execution remain incomplete.

The future development of intelligent oilfields will primarily focus on three aspects: (1) data integration and governance, requiring the breaking down of data silos to achieve high-quality, cross-system and cross-scale data consolidation; (2) hybrid modelling and intelligent optimization, combining physical modelling, data-driven approaches and operations research optimization to enhance the interpretability and safety of decision-making; (3) technological synergy and closed-loop application, where the integration of deep reinforcement learning, digital twins, and cloud-based decision platforms will establish a dynamically adaptive intelligent oilfield system, achieving a closed-loop cycle of prediction, optimization, execution, and feedback. Concurrently, industrial applications must continue to focus on large-scale deployment, management system optimization, and balancing environmental and economic benefits. Overall, intelligent oilfields are progressing from localized to systemic intelligence, with their technological convergence and application deepening set to become a crucial pillar for the high-quality development of the oil and gas industry.