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In unconventional wells, returns are driven in part by the reduction of variability in efficiency and performance. In 2021 the stimulation of two wells in the Bakken proceeded under the architecture of an automated frac control system communicating directly to Machine Learning predictive models at the headquarters of the well operator. This represents the first time an algorithmic frac was conducted via automation, adjusting stage designs, and pushing those stage designs to the field without human intervention. This paper discusses a fully integrated and automated completion performed on the operator's four- well pad in the Bakken. It reviews the impact on completion performance, completion design, components of the system and execution. Throughout the completion, automated software interfaced with the frac control system executing the job. Additionally, data was uploaded live and fed to the Machine Learning predictive model. This allowed the model to learn from actual well data and suggest improvements. Improvements were captured, iterated on, and design updates were sent back to the control system for the next stage in the completion sequence. Human oversight was conducted but only as a check, during the entire process. Both the automated frac control system and algorithmic design system were functionally separate but communicated live, allowing the operator to take advantage of their complete basin knowledge database without compromising data integrity and model confidentiality. Additionally, sensors provided real-time data such as treating pressure, rate and proppant concentration, as well as downhole data such as cluster uniformity, fracture geometry, and offset well interactions. The project was launched with several primary goals in mind: First was to functionally test the automation of the frac fleet for the operator proving its ability to consistently place their designs.Second was to incorporate the prediction model algorithms into completion design and test how quickly and how much the Machine Learning models could actually learn from actual well stages. Both of these primary goals were achieved, validating the ability to automatically execute completions and to tie design changes live to a control system elsewhere. This represents the first time a hydraulic fracture was conducted via automation with algorithmic integrated design improvement, either independently or together. These capabilities can improve execution and performance where it is becoming increasingly difficult to deliver step changes in well performance with current manual crews and technology. Integrated automation provides an upgrade to completion performance by reducing variability in execution and well performance while also enabling tailored designs on scales previously unattainable.
In unconventional wells, returns are driven in part by the reduction of variability in efficiency and performance. In 2021 the stimulation of two wells in the Bakken proceeded under the architecture of an automated frac control system communicating directly to Machine Learning predictive models at the headquarters of the well operator. This represents the first time an algorithmic frac was conducted via automation, adjusting stage designs, and pushing those stage designs to the field without human intervention. This paper discusses a fully integrated and automated completion performed on the operator's four- well pad in the Bakken. It reviews the impact on completion performance, completion design, components of the system and execution. Throughout the completion, automated software interfaced with the frac control system executing the job. Additionally, data was uploaded live and fed to the Machine Learning predictive model. This allowed the model to learn from actual well data and suggest improvements. Improvements were captured, iterated on, and design updates were sent back to the control system for the next stage in the completion sequence. Human oversight was conducted but only as a check, during the entire process. Both the automated frac control system and algorithmic design system were functionally separate but communicated live, allowing the operator to take advantage of their complete basin knowledge database without compromising data integrity and model confidentiality. Additionally, sensors provided real-time data such as treating pressure, rate and proppant concentration, as well as downhole data such as cluster uniformity, fracture geometry, and offset well interactions. The project was launched with several primary goals in mind: First was to functionally test the automation of the frac fleet for the operator proving its ability to consistently place their designs.Second was to incorporate the prediction model algorithms into completion design and test how quickly and how much the Machine Learning models could actually learn from actual well stages. Both of these primary goals were achieved, validating the ability to automatically execute completions and to tie design changes live to a control system elsewhere. This represents the first time a hydraulic fracture was conducted via automation with algorithmic integrated design improvement, either independently or together. These capabilities can improve execution and performance where it is becoming increasingly difficult to deliver step changes in well performance with current manual crews and technology. Integrated automation provides an upgrade to completion performance by reducing variability in execution and well performance while also enabling tailored designs on scales previously unattainable.
Historically, oil and gas production and optimization were conducted on-site, with the control center located close to the facility. This creates issues with collaboration, command silos, and redundant tools, particularly in remote offshore locations. Several businesses have reported that integrated operation centers (IOC) are a viable option, but success is not guaranteed. This article discusses the challenges and best practices involved in executing a successful IOC initiative in Thailand. We analyze successful IOC initiatives as well as their difficulties and solutions throughout project planning, design, and execution. Significant challenges were identified by using the design thinking process alongside multiple site visits and user interviews. The difficulties include a shortage of collaboration, redundant optimization tools, traditional technological barriers, and an abundance of non-prioritized offshore activities. Key ideas have been reviewed and high priority subjects are identified. The establishment of centralized control room in Bangkok office to control three remote (400 km) offshore sites and streamlined digital workflows to create collaboration cultural shift are selected to solve the major issues. Traditional optimization efforts frequently have a common root cause, which is the silo chain of command from various control center locations located near offshore assets. Diverse digital tools are developed and implemented independently, without any centralization. In the past, it was expensive to set up remote control centers far from production platforms. However, advancements in communication technology, such as fiber optics, have reduced the cost of providing a remote IOC project by about 20 times. Data technology has progressed to the point where we can now investigate the specifics of overdue work orders, and manual process monitoring has been digitally transformed with an automated, data-driven system. For our Thailand IOC project, we relocate panel men, activity planners, marine control personnel, and engineers from three separate offshore locations into the single control center in our Bangkok office using remote control technology. Transformation of the collaboration culture is facilitated by daily optimization meetings and in-depth team discussions. Shared and streamlined best practices from three distinct offshore locations have improved production efficiency. Digital technologies are upgraded and condensed into single optimization tool with prioritized work orders for physical planning. Even with significantly less personnel, the number of overdue work orders was significantly reduced. This project addresses key challenges as part of implementing a new IOC Thailand project. The lessons learned and best practices are consolidated and reported as part of this research along with a case study from our project. This paper is beneficial for anyone who is planning to pursue their IOC project as well as those who want to improve and benchmark the centralized optimization effort. It includes best practices to design and use new operating model, facilities, and digital tools to overcome the future business difficulties, improve production efficiency, enhance maintenance planning, and enrich personnel safety with reduced manpower.
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