Drilling operation started drilling more challenging wells that is farer, deeper and in unconventional conditions; this require the drilling industry to adopt new technologies supporting them in these challenges. One of the big potential supporting technologies is the artificial intelligent. Artificial intelligent could help drilling engineers and operation crew in crunching the massive amount of drilling data converting them to decision-like format, leading to safer, faster and more cost effective operations. The challenge is that artificial intelligent projects consists of multi dimension tasks, starting from data handling, infrastructure building, through model development and integrating with existing environment. Such tasks confuses even IT teams, so developing artificial intelligent projects targeting drilling domain will be very tough. Experience shows that a lot of oil & gas artificial intelligent projects fails due to miscommunication between the business domain experts and the artificial intelligent, not having common understanding, problem in the data, or model computability issues could be also other reason for such failure.
This paper propose a methodology that will increase the possibility of having success artificial intelligent drilling project. This methodology is CRISP-DM which stand for Cross Industry Standard Process for Data Mining. This methodology consist of the following phases Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. This paper is shading lights on these phases, also it will derive the readers through a drilling case-study, where this methodology was applied leading to successful artificial intelligent drilling project.