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By Yogi Schulz
Generative artificial intelligence (AI) is sweeping through the oil and natural gas industry. It’s out of control, like the Wild West. AI output is showing up in reports and presentations. The Apple App Store and Google Play offer many free AI apps of varying quality. Every AI software vendor provides access to their prompt website. AI output is part of search results. AI capabilities are integrated into desktop software.
By implementing AI applications, companies are transforming operations, from optimizing exploration and drilling to streamlining production and logistics. Through advances in machine learning, big data analytics, and automation, companies benefit from efficiency, safety, and environmental sustainability.
Most companies will be better off rolling out vendor AI solutions as they become available rather than designing and implementing an entirely new and exceedingly ambitious custom AI application.
What are some practical applications where the oil and natural gas industry could speed up the adoption of AI? You will immediately recognize that none of these applications are new. The value that AI contributes to these applications includes:
- More confident or accurate results.
- Reduced elapsed time to achieve the results.
- A significant reduction in staff effort to achieve the results.
- Increased scope or scale in factors such as the number of entities such as wells or facilities, higher data resolution or larger geographic area.
This first article describes significant AI applications for the first three phases along the upstream exploration and production life cycle that optimize operations and reduce costs to increase margins. This second article will describe AI applications for the remaining three phases. The upstream life cycle consists of these significant phases:
- Exploration
- Field development
- Well drilling and completion
- Production operations
- Abandonment
Consider which AI applications could bring the most value to your producer organization.
Exploration
The introduction of higher resolution seismic and well log data gathering has dramatically increased the associated data volumes. AI supports higher resolution interpretation and manages the additional data volumes.
Seismic processing
Seismic data volumes are overwhelming geoscientists. AI image processing can process, interpolate, and interpret large seismic data volumes to identify key attributes of subsurface structures. Example attributes include acoustic impedance, amplitude, horizon tracing, fault location, or direct hydrocarbon identification. The AI benefits of seismic processing and interpretation include:
- Making more informed decisions about allocating exploration budgets and avoiding poor prospects.
- Decreasing the amount of data needed for high-resolution seismic data while increasing the quality of the interpretation.
- Reducing the elapsed time for seismic data processing and interpretation.
- Reducing the effort of seismic data interpretation staff.
Digital well log evaluation
Well log data volumes are overwhelming well log analysts. AI can search a digital well log database containing millions of well logs for multivariate patterns of interest to identify hydrocarbon-bearing zones and estimate porosity, permeability, lithology, and fluid content. Then, geotechnical staff can visualize and further analyze the results. The benefits of adding AI to digital well log evaluation include:
- Making more informed decisions about where to drill and avoid marginal opportunities.
- Identifying patterns of interest that would easily be missed by geotechnical staff.
- Reducing the elapsed time of the evaluation.
- Reducing the interpretation effort of geotechnical staff.
Field development
The availability of satellite maps, light detection and ranging (LIDAR) data, and geographic information systems (GIS) has added analytic capability to development teams. Field development plans the locations of wells, pipelines, processing facilities, disposal facilities, roads and utilities. AI improves the analysis of development scenarios and manages the additional data volumes.
The benefits of adding AI to field development include:
- Modelling multiple complex scenarios quickly.
- Reducing the elapsed time for development.
- Controlling capital and operating costs.
- Minimizing environmental impact.
Well drilling and completion
The advent of horizontal wells and hydraulic fracturing has added considerable complexity to well drilling and completion. AI improves the more complex data analytics and manages the additional data volumes.
Well planning
Petroleum engineers invest significant time in well planning because of the many objectives and variables involved in drilling and completion.
Example variables for drilling include total length, height of target formation, number of bends, number of drilling pipe strings, corrosion issues, and types of formations being penetrated.
Example variables for completion include hydraulic fracturing fluid characteristics and volume, proppant characteristics and volume, number of fracturing stages, formation pressure and temperature.
The benefits of adding AI to well planning include:
- Modelling multiple scenarios quickly.
- Reducing the risk of blowouts.
- Reducing the risk of expensive stuck drill pipe incidents.
- Controlling capital and operating well costs.
- Ensuring worker safety.
Well drilling
Petroleum engineers monitor well drilling in real time to observe progress and deviations from the drilling plan. They track variables such as mud weight and pressure, rate of penetration (ROP), rotary speed and torque, and actual well path compared to plan.
The benefits of adding AI to well drilling include:
- Minimizing deviations from the wellbore plan.
- Improving the likelihood of successful completion work.
- Reducing the risk of accidents such as blowouts and stuck drill pipes.
- Controlling well drilling costs.
Well completion
Petroleum engineers monitor well completion in real time to observe progress and deviations from the completion plan. They track variables such as water injection rate and pressure, proppant pressure and density, and reservoir pressure and temperature.
The benefits of adding AI to well completion include:
- Maximizing reservoir porosity improvement.
- Minimizing formation damage.
- Controlling well completion costs.
AI applications offer oil and natural gas producers an enhanced ability to increase revenue, reduce cost and control risk in all phases along the upstream exploration and production life cycle.
Yogi Schulz has over 40 years of experience in information technology in various industries. He writes for Engineering.com, EnergyNow.ca, EnergyNow.com and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, the need to leverage technology opportunities, and mergers. His specialties include IT strategy, web strategy, and systems project management.
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