The oil and gas sector has been steadily advancing along its digital transformation journey, albeit with specific areas such as subsurface operations still ripe for further innovation. Despite the strides made with the aid of advanced cloud technologies and artificial intelligence (AI), the industry faces a multitude of challenges that hinder swifter progress. These include talent shortages, a general scepticism towards AI, outdated infrastructure, and proprietary data constraints. Nonetheless, industry leaders like Swapnil Bhadauria and Abhilash Krishna at Ernst & Young LLP are advocating for impactful strategies to overcome these hurdles.
Subsurface operations involve high-stakes decision-making processes that depend heavily on complex data analysis. The move towards digital transformation in these processes is driven by two main themes: prospect identification and asset characterisation, and drilling, completion, and production efficiency management.
In prospect identification and asset characterisation, advances in AI are revolutionising the way seismic data is handled. AI algorithms, particularly those employing deep learning models, can now automate the processing of 3D/4D seismic data, enhancing signal-to-noise ratio and improving the identification of geological features and hydrocarbon indicators. Well log and core data analysis also benefit from AI, with sophisticated algorithms efficiently identifying lithology, detecting hydrocarbon zones, and estimating reserves with greater accuracy than traditional methods. Furthermore, AI’s role in subsurface imaging and mapping has considerably improved geological mapping, crucial for identifying potential drilling targets.
Drilling and completions (D&C) optimisation is another area seeing significant benefits from AI. By analysing real-time data from drilling operations, AI can optimise the rate of penetration, reduce bit wear, and prevent operational hazards. Additionally, AI aids in efficient well path design, enhancing the drilling of horizontal wells by managing costs and avoiding collisions while maximising hydrocarbon recovery. Production forecasting, a vital aspect of efficiency management, is now more accurate thanks to AI models that leverage both historical and real-time data.
Moreover, AI is instrumental in enhancing oil recovery strategies and predictive maintenance. By simulating various enhanced oil recovery (EOR) scenarios, AI helps select the most effective techniques, thereby increasing the recovery factor in mature fields. Predictive maintenance leverages AI to foresee equipment failures, reducing unplanned downtime and extending the life of infrastructure.
Despite these advancements, the adaptation of these technologies in subsurface operations remains slower than desired. Bhadauria and Krishna suggest several strategic focus areas for companies to accelerate the transformation curve. They recommend leveraging existing investments to pilot scalable AI-driven use cases, highlighting the importance of building trust in AI outcomes to deliver business value swiftly. Additionally, they stress the need for ongoing maintenance of AI systems and the management of data integration within operational workflows.
An essential component of this transformation is fostering a culture that embraces AI. This can be achieved by educating employees on how AI can enhance their roles and promote a more rapid adoption of these technologies. Finally, the suggestion is made for companies to adopt a decision-centric data approach, focusing on validated AI solutions for widespread integration, while reserving in-house development for unique proprietary technologies.
By embracing these strategies, the oil and gas industry can continue to push the boundaries of digital transformation, ensuring more efficient and effective subsurface operations. Bhadauria and Krishna's insights underline the critical importance of innovation and adaptability in navigating the complexities of the industry’s digital future.
Source: Noah Wire Services