How artificial intelligence can revolutionizes oil and gas exploration and reservoir management

Introduction

Artificial intelligence (AI) has the potential to revolutionize the oil and gas industry by enhancing exploration and reservoir management processes. Oil and gas industry is facing huge challenges from new emerging options of fuel like hydrogen for vehicles, cooking gas; lithium for vehicles. Competitive hydrogen fuel production prices can have big impact on oil and gas industry businesses as a whole. Due to this oil and gas industry needs to become more cost competitive to remain in the fuel business. Here are several ways AI can bring about significant improvements in the businesses of hydrocarbon industry.

Data analysis and interpretation

AI can analyze vast amounts of geological, geophysical, and reservoir data to identify patterns, anomalies, and correlations that may be difficult for humans to detect. AI systems can detect subtle patterns and anomalies that might indicate the presence of hydrocarbon reservoirs. By leveraging machine learning algorithms, AI can quickly process and interpret seismic surveys, well logs, and production data to generate insights and make informed decisions. These algorithms can learn from historical drilling and production data, enhancing their ability to identify promising drilling locations. This significantly reduces the time and effort required to assess seismic data and generates more accurate predictions.

For example, a company operating in an offshore basin may have access to terabytes of seismic data from previous surveys. By applying AI algorithms, the company can rapidly analyze this data, identifying potential areas with high hydrocarbon prospects. The AI system can consider various geological factors, such as structural traps, stratigraphic features, and fluid migration pathways, to generate a map of high-potential drilling locations (Figure 1).

Figure 1. AI technology can utilize various geological factors, such as source, reservoir rocks, structural traps, stratigraphic features, and fluid migration pathways to predict potential borehole locations.

Geophysical imaging and interpretation

In traditional seismic interpretation, geoscientists manually analyze seismic data to identify potential drilling locations. However, this process is time-consuming and subject to human bias. AI algorithms can improve the accuracy and efficiency of seismic imaging and interpretation. AI techniques can revolutionize this aspect by automating seismic interpretation and prospect generation. Evaluating large volumes of seismic data, AI models can enhance the quality of subsurface images, identify potential hydrocarbon-bearing structures, and reduce uncertainties in reservoir characterization.

Figure 2. AI technology can improve the accuracy and efficiency of seismic imaging.

Looking at both seismic sections (From a relinquishment report of Block 20/7a (License P.272) of Nexen Petroleum U.K. Limited, 2007) we can distinguish the difference in the quality of these seismic sections (Figure 2). Analyzing these seismic sections indicates that the raw seismic data contains much information which is not surfaced in seismic sections and cannot be visualized and analyzed due to inappropriate seismic processing techniques. Here AI based processing technology can improve the accuracy and efficiency of seismic imaging saving time as well as money.

Drilling optimization

AI algorithms can analyze real-time drilling data, including parameters such as weight on bit, rate of penetration, and wellbore stability, to optimize drilling operations. By continuously monitoring and analyzing these parameters, AI systems can make recommendations for adjusting drilling parameters in real-time, improving drilling efficiency, reducing drilling time, and minimizing costs. 

AI systems can monitor real-time data from various sensors, such as temperature, pressure, and flow rate sensors, to detect anomalies and abnormalities. By continuously analyzing this data, AI algorithms can identify equipment malfunctions, production irregularities, or safety hazards in real-time, allowing for timely intervention and mitigation of potential issues.

Reservoir simulation and optimization

AI can enhance reservoir simulation models by incorporating complex geological and engineering data. By analyzing historical production data, well performance, and reservoir characteristics, AI algorithms can provide accurate predictions of future production rates and optimize production strategies accordingly. This helps operators plan for production targets, allocate resources effectively, and optimize field development plans.

Operational efficiency and cost reduction

AI-driven systems can analyze sensor data from drilling equipment and machinery to detect patterns indicative of potential failures. AI can optimize operational processes and reduce costs across the oil and gas value chain. By leveraging AI-powered algorithms for production planning, maintenance scheduling, and supply chain management, companies can streamline operations, minimize downtime, and optimize resource allocation. AI-powered natural language processing techniques can analyze unstructured data sources, such as scientific literature, research papers, and industry reports, to extract relevant knowledge and insights. This enables operators to stay updated with the latest advancements, research findings, and best practices in the field, fostering innovation and informed decision-making.

Risk management and safety

AI can contribute to risk management by analyzing historical data, identifying potential hazards, and predicting equipment failures or safety incidents. By implementing AI-powered predictive maintenance systems, operators can detect equipment anomalies and schedule maintenance proactively, reducing the risk of costly failures and ensuring worker safety.

Conclusions

Artificial intelligence (AI) has the potential to transform oil and gas exploration and reservoir management by enabling faster and more accurate decision-making, optimizing operations, reducing costs, and maximizing hydrocarbon recovery. However, it's important to note that successful implementation of AI technologies requires access to high-quality data, robust computing infrastructure, and domain expertise to interpret and act upon the insights generated by AI systems. By leveraging AI's capabilities in machine learning and data analytics, companies can do significant improvements in operational efficiency, cost reduction, and decision-making processes. With the help of AI technologies hydrocarbon companies can compete with the other companies who are eating the business and market share of hydrocarbon industries.