Digital technologies are helping the customers in an unbeatable way to develop a project from the conceptualization stage up to the evaluation phase once the job is completed. This approach is affecting the entire upstream production cycle.
From the customer’s perspective, more accuracy on the planned time frame and costs, can be achieved through big data analysis during the planning phase. Later on at the execution phase, more efficient and easy decision making is accomplished based on AI models; and finally during the evaluation phase, better and detailed KPIs are set to evaluate every stage of the development of a project applying Machine Learning (ML) models. All these milestones have been fulfilled using modern techniques that can be replicated and customized on different projects. Thus, providing a clear advantage on improving the AI model’s accuracy and performance as more data is acquired day by day.
The drilling process is dynamic, and as such, there are many variables that affect it directly and indirectly. These variables are changing with every well drilled, every new environment and every new challenge. The purpose of each company and its drilling team is to understand them, how they interact with each other and how they can be managed to control risks and optimize costs to develop a profitable project. To reach a successful project, the reduction of risks is a must; this milestone can be achieved through the connection of human expertise, proper utilization of historical data, and implementation of new technologies. One of the most practical and efficient methods to include historical data, is to review previous experiences in terms of planning, performance, and execution.
With the current situation of the O&G market, more challenging projects need to be executed with the highest efficiency possible; being unexplored and harsh zones some examples, where unfortunately, the closest well is sometimes located far away from the well to be drilled, therefore, the access to offset data is very limited (i.e., old, not accurate, partial or out of date). In projects like these, the expertise of the drilling personnel plays an important role. All the events, problems, and assumptions, are recorded and analyzed during the planning phases and later implemented on the execution phase. The objective is to avoid masking the risk, and turning an otherwise profitable project into non-profitable or high risk. Every project varies according to different team member’s knowledge and expertise. Here is where the new digital technologies that utilize AI powered automation can facilitate the ability to extract, analyze, compare, identify and create trends from a single database. In return, this allows us to obtain the desired outputs such as: drilling parameters maps, drilling optimization, early problems identification alerts, formation signatures, and logging curves. For example, something very useful and relatively easy to create in all drilling operations, is a parameters road map. With the AI & ML technics, this road map will not only involve the safest parameters to drill every formation, but also can involve the proper combination of parameters to maximize the ROP, increase longevity/reliability of the downhole tools, identify in real time lithology changes etc; in fact, some models can generate real time logging curves, that could be used as a LWD back up data. All these features can be monetized in terms of risk reduction and increased efficiency, which translate into big opportunities to reduce costs in an exploratory Deepwater well.