Leveraging automation to boost field productivity, enhance operational efficiencies

With a focus on reducing environmental impact and decarbonization across the globe, the energy industry is working aggressively to address this generational challenge while also tackling two additional challenges with global impact: ensuring energy access and security, and ensuring energy affordability. Growing demand exacerbates each of these challenges, with the United States Energy Information Administration (EIA) estimating that energy demand will increase nearly 50% over the next 30 years.  

The EIA predicts that power generation from renewable energy sources like wind and solar will increase from approximately 21% today to 44% in 2050, while nuclear energy will decrease from approximately 18% to 12%. Power generation from fossil fuel sources will continue to play a major role in the total energy mix, with the EIA projecting a decrease from 60% today to 44% in 2050.  

For the oil and gas industry, there’s an increasing emphasis on OPEX-led production. Operators are looking to accelerate innovation as a means to improve recovery from existing resources and infrastructure. By boosting production from underperforming and mature fields, operators can increase return on investment of existing infrastructure without adding additional CAPEX. The new reality is that the cheapest, shortest cycle and least carbon-intensive barrel won’t be delivered by drilling new wells—it will come from optimizing the thousands of mature and underperforming wells worldwide. 

Embracing this new reality will require more than incremental improvements—the industry must transform its approach to field production to drive down the costs and carbon-intensity per barrel produced. By leveraging digitalization at scale, combined with deep domain expertise, and integrating data from across the hydrocarbon production lifecycle, the industry can break down data silos and move beyond the optimization of individual parameters—one well at a time—to an integrated, data-driven, full-field, production optimization approach. 

Transformation through automation. Across nearly every industry, organizations are utilizing digital technologies to automate tasks and processes, leading to higher levels of production, improved productivity, more efficient use of materials, and improved health and safety. Given the remote and sometimes challenging environments in which the oil and gas industry operates, automation of specific tasks provides a clear pathway toward minimizing environmental impact and safety risk, optimizing workforce planning, and enhancing overall field performance to get maximum return from existing assets. 

This is particularly true in the production domain, where leveraging digital solutions and domain expertise are crucial for full field optimization. Investing in production automation yields a tangible return on investment by improving asset uptime, reducing downtime and equipment failures, and maximizing reservoir productivity while monitoring and controlling emissions. Considering that the overall production lifecycle can last upwards of 20 or 30 years, any percentage gain in performance or reduction in carbon-intensity aggregated over this period leads to significant and relevant absolute values. 

Throughout the duration of the production lifecycle, operators are dependent upon staff physically visiting field locations for routine operations like equipment monitoring and maintenance, data collection, and routine operational requirements. Many of these tasks are time-consuming and redundant, and create avoidable health and safety exposure in addition to carbon emissions. The industry can minimize physical trips to the field by sending crews only to wells requiring service by using predictive analytics leveraging machine learning (ML) and artificial intelligence (AI) models and combining with real-time field data to automate other tasks. 

Automating field production, however, doesn’t come without complexities. Production is impacted by various factors, from varying reservoir behaviors and subsurface equipment performance, to flow assurance and surface handling of produced fluids, through transmission of hydrocarbons to the point of treatment or usage—leading to significant data management challenges. In balancing the above factors, operators face difficult trade-offs and a wide range of options and scenarios. Analyzing the scenarios to find the optimal solution is critical, and yet the human capital with the experience and competency to do that analysis is in decline.  

Historically, the industry has split these various stages of the production lifecycle into smaller domains managed by their own groups of subject matter experts. This has led to siloed approaches, strategies and solutions, and siloes within organizations. The industry has effectively optimized each of these domains, but it has long-sought a transformational solution capable of integrating data from activities across the production lifecycle to enable true full-field production automation. This means an ability to have fewer—but digitally enabled—individuals rapidly analyze many scenarios and receive clear recommendations—recommendations that, with the proper controls in place, can be implemented with automation. Today, through the advances of digital solutions, big data, ML and AI, the industry has the tools required to overcome this decades-long challenge of breaking down siloes and transforming production operations. 

This was the key driver behind the recently announced strategic collaboration between Baker Hughes and Amazon Web Services (AWS) to develop the Leucipa automated field production solution, Fig. 1. The cloud-based and data-driven solution helps to optimize production by supporting operators with recommendations and orchestration of artificial lift equipment, power supply, chemical consumption, water handling, inflow control valve settings, prioritized work scheduling, and advanced analytics that predict interactions between wellbores and surface facilities. Doing so enables optimization of resources—human capital, OPEX and CAPEX—resulting in minimized health and safety exposure, while also driving down the cost and carbon-intensity per barrel produced.  

Fig. 1. The Leucipa automated field production solution helps to optimize production by supporting operators with recommendations and orchestration of artificial lift equipment, power supply, chemical consumption and water management. It also prioritizes work scheduling and advanced analytics that predict interactions between the wellbore and surface facilities.

Automating field production. The collaboration brings together Baker Hughes and its global footprint and decades of domain expertise and leadership in areas like artificial lift and chemical injection and AWS with its computing power and digital tools needed to aggregate and manage data from the various interdependencies tied to production, Fig. 2. Both organizations, in their own right, are digital technology innovators, developing new solutions and approaches that are helping to transform industries.  

Fig. 2. The Leucipa solution combines the extensive domain expertise of Baker Hughes, with the strength of the AWS cloud.

Integral to automation is ensuring consistency across repetitive operations, and doing so requires a continuous loop to measure, analyze, and then act. To acquire data, the automated field production solution utilizes Internet of Things (IoT) technologies like instrumented sensors and smart valves. Analysis occurs in the cloud, where ML and AI tools are used for data integration and continuous monitoring of field production performance, and then suggests the action, based on personalized recommendations that will help drive production optimization and efficiencies. In the execution of the above, the Leucipa solution will greatly accelerate the scale and speed of an operator’s continuous optimization loop (measure, analyze, act).   

Reflecting the oil field’s heterogeneous makeup, a core tenet of the solution is that it must be open and agnostic to field equipment manufacturers. This core tenet of openness is also evident in the creation and ongoing development of the automated field production solution. Initial development is based on extensive feedback and input from customers, including surveys with subject matter experts and interviews with more than 160 oil and gas operators.  

The Leucipa solution will continuously learn and get smarter as new data are collected and analyzed: new insights can be created; previous insights can be improved. Further to this continuous development, Baker Hughes and AWS are leaning on a customer advisory board comprised of engineering experts and key decision-makers to provide input and ensure each instance of the solution is particularly suited to accommodate operator requirements and necessities. The iterative approach for releasing new features is also integral to users providing a solution that is continually improving and adapting to new ways of working. 

Just getting started. Initially, the Leucipa solution will focus on artificially lifted wells and on production chemicals, given the core strengths of Baker Hughes and the large potential impact of improvements in these areas. But Leucipa is not merely a well optimization solution. Leucipa will evolve to automated controls, with consideration of surface constraints (such as water handling or takeaway capacity, power), to enable full-field optimization. Reservoir forecasts over time will further provide context for the optimization of OPEX.  

Another key early focus of the solution is addressing emissions associated with production. Leucipa will allow operators to tackle emissions in several ways, Fig. 3. By analyzing real-time data from field assets and equipment, operators can identify and possibly predict emission events like tank venting, valve control venting or flaring. Traditionally, operators rely on visual inspection at sites and tank farms, which may leave some emissions issues undetected and unresolved. By applying ML and AI models to operational data, operators can anticipate certain events, and work proactively to mitigate potential emission impacts. 

Fig. 3. By automating field production, the solution improves performance and energy efficiency, which will reduce emissions. The technology also minimizes health and safety exposure by reducing trips to the field. AWS is Baker Hughes’ preferred and recommended cloud provider for the automated field production solution.

The solution also can be integrated with field-based methane monitoring solutions to address emissions. Operators currently use ground-based methane sensors at well sites, data from fixed-wing and drone aircraft, and satellite spectral imaging. Combining the Leucipa solutions operational data with these sources offers a multi-dimensional emissions view, enabling operators to quickly respond and mitigate impacts. 

The solution is being developed and released in an agile way, with development driven initially by operators on the customer advisory board. Early deployments provide operators a single pane of glass with data integration and recommendation-driven optimization for artificially lifted wells. It will include predictive failure analysis for electric submersible pumps, as well as initial elements of power optimization. Pan American Energy, a large integrated energy company based in Argentina, is the first of a number of operators that have signed on to support and pilot the automated field production solution. 

Significant value potential. While still in the early stages, the journey toward automated field production is well underway. The data-driven Leucipa solution will enable operators to proactively manage and automate field production operations, optimize the use of resources—including human capital, power consumption, and chemicals. It will also improve life-of-field activity scheduling, helping to extract maximum value from their fields, minimize health and safety exposure and reduce the cost and carbon-intensity per barrel produced, Fig. 4. This OPEX-led production approach will play a key role in addressing the industry trilemma of meeting growing energy demand, ensuring energy security and affordability, and reducing the environmental impact associated with oil and gas production. 

Fig. 4. The Leucipa automated field production solution will enable oil and gas operators to address the industry's increasing demand for scalable, automated approaches to increasing production, improving energy efficiency and reducing emissions.