The use of Artificial intelligence in oil and gas industry is already having a profound effect on the sector.
With the top 20 global oil and gas companies leading the charge, AI is gaining widespread adoption due to the efficiency it introduces across different operational spectrums.
Exploration for hydrocarbon reserves is one of the critical processes set to benefit from AI-driven solutions. This high-stakes activity is full of financial risks and demands cutting-edge technologies to optimize success rates and offset risks.
Commercial exploration success rates were recorded at 29%-40% between 2020-20211. Unsuccessful explorations can cost between $5 million to $20 million2, underscoring the need for higher success rates.
This article looks at the applications of AI in oil and gas exploration. Additionally, we examine how leading companies leverage AI to optimize workflows and redefine industry standards.
Applications of AI in oil and gas exploration
Technological advancements such as AI are revolutionizing how upstream companies conduct exploration activities. From seismic analysis to reservoir modeling, AI continues to help the industry optimize its operations.
Here are the top 5 applications of AI in oil and gas exploration.
AI-powered geological assessment
The assessment is crucial in estimating the hydrocarbon resource and decision-making. However, finding minor faults that may be resource-rich can be a tedious task that demands analyzing vast datasets. Traditional methods depend on human analysts and take a lot of time.
AI-powered solutions aid with targeted exploration tasks such as fault finding. Such tools can analyze satellite and historical exploration data to identify areas with potential deposits. Using neural networks, models can identify patterns and subsurface anomalies, helping to find suitable exploration sites quickly.
Other AI approaches, such as non-gradient optimization, have been used to create tools for reservoir rock mapping. AI speeds up the process from weeks to seconds while drastically reducing the chances of wrong mapping due to human errors3.
Additionally, geological assessment maps the subsurface to identify potential reservoirs. The process provides different datasets such as seismic and geological data, satellite imagery, and well logs. The seismic interpretation of 3-D seismic volumes usually takes weeks or months [4].
Deep learning systems can analyze such data and rapidly identify specific features from geological assessments. They can accelerate the data interpretation by a factor of 10-1000 [5]. Pattern recognition techniques can identify features like fault probability volumes that indicate hydrocarbon presence [6].
For instance, IBM developed an intelligent tool to analyze vast datasets of unstructured geological data for Wintershall Dea. The tool utilized machine learning(ML) and Natural Language Processing(NLP) to build a solution to help experts make decisions during the early phases of oil and gas exploration. The AI-powered advisor assists in predicting the expected oil and gas volumes.
