Oil and Gas
Commodity cycles, capital planning challenges, and increasing operational risks in the oil and gas industry are all factors that make it more important than ever to make smarter and more efficient decisions. With AI and machine learning, companies can deliver the returns investors require, improve return on assets, and manage downside risks by turning much of the data already collected into usable and valuable insight.
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AI in Oil and Gas
With the dynamic landscape for energy production, AI provides powerful benefits across the entire value chain. AI helps oil and gas companies assess the value of specific reservoirs, customize drilling and completion plans according to the geology of the area, and assess risks of each individual well. In addition, downstream operations can be optimized to minimize costs and maximize spreads.
Exploration and Production
- Forecast total recoverable reserve volumes
- Analyze exploration and reservoir data
- Model well spacing and field development plans
- Optimize lateral and frac design
- Model and simulate various proppant and fluid loading options
- Create lifetime well production models and more effective production forecasts
- Set bidding strategies for lease blocks based on market behaviors
Midstream and Refining
- Forecast long-term commodity input and product market price
- Provide capital planning and risk evaluation for better long-term decisions
- Optimize commodity trading and hedging strategies
- Improve reliability risk modeling for refining and processing assets
- Maximize labor productivity and wrench time
- Enhance asset scheduling for refining and processing operations
- Optimize pipeline scheduling for product flows
Oil Field Services and Equipment
- Optimize drilling, completion equipment scheduling, and fleet management
- Manage and optimize supply chains
- Optimize procurement strategies for proppant, water, and other consumables
- Identify root causes and drivers of non-productive time
- Forecast customer demand and drilling activity in the medium- and long-term
- Enhance back-office and invoicing/billing processes
- Automate financial controls for high-volume transactions
AI Use Cases for Oil and Gas
The oil and gas industry is beginning to see the incredible impact that AI can have on every sector in the value chain. The opportunities for AI strike directly at the greatest challenges in today’s oilfield. Companies that effectively leverage AI will have a distinct advantage over other operators that lack accurate understanding of their reservoirs, operating processes, and producing assets.
Armed with AI, operators can better understand their reservoirs and minimize geologic risk. There is tremendous, but untapped, value in the data collected today. Operators can use it to make better exploration and production decisions, and optimize acquisition strategies with better forecasts of lease transaction prices.
Drilling and Completions
AI has proven to be very effective at improving well design, drilling execution, and completion execution. Producers can maximize ROI for every well by optimizing well placement and well spacing to maximize resource recovery, designing wells to optimize recovery and total cost, and predicting sub-surface risks.
Accurate daily, monthly, and lifetime well production forecasts are critical for successful production. Machine learning can help to optimize flow rates, pressure, and other variables for maximum lifetime well production. Plus, anomaly detection capabilities allow operators to anticipate well issues in advance before they cut off production.
Gathering and Transportation
AI helps operators forecast product flow, demand, and price to make long-term capital decisions based on product supply-demand imbalances and local market price spreads. They can also model right-of-way (ROW) acquisition costs and improve planning and routing with more informed estimates of easement costs.
Processing and Refining Maintenance
In order to optimize processing and refining processes, operators are using AI for shutdown planning at their refineries. They can model and quantify the risk of failure for key equipment in the critical path during maintenance shutdowns to make more informed decisions about scope, reduce total shutdown cost, and improve equipment reliability.
Corporate and Back-office
AI can have a huge impact on the front lines, but its impact behind the scenes can be just as powerful. Operators use AI to forecast commodity prices for capital project planning, risk management, and marketing activities, as well as anticipating potential health and safety risks. It has also proven effective at automating high-volume vendor invoice analysis and processing to reduce costs and identify errors.
DataRobot Can Help:
Exploration and Production (E&P) Companies
Enterprise AI can help E&P companies estimate the potential value of reserves, taking into account the cost of acquisition, production, and transportation, and more. It can make recommendations on whether it’s better to explore and develop further or walk away, either saving or creating investment value.
Oilfield Services and Equipment (OFSE) Companies
AI can help OFSEs manage risk and optimize operations. It can predict supply chain delays, equipment failures, commodity price changes, and customer demand with better forecasts; or develop value-added services for customers to help them improve the unit economics of their reserves and extracted hydrocarbons.
Midstream and Downstream Operators
Midstream and downstream operators can utilize AI in all aspects of their business – from optimizing the processing of raw inputs to transport strategies between production locations and refining and processing locations. Highly accurate predictive models can forecast the need for maintenance to minimize downtime of critical operating equipment.
Enterprise AI can predict supply and demand for commodities and compare it with forecasts it creates for price trends. This insight allows traders to maximize profit by leveraging arbitrage opportunities across place and time.
Integrated Oil and Gas Companies
Majors, Super-Majors, and other vertically integrated oil and gas companies can benefit from all of the above activities, as well as other corporate and back-office activities to make AI-driven decisions part of the fabric of all operations.
DataRobot’s platform makes my work exciting, my job fun, and the results more accurate and timely – it’s almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.