Well construction requires the cooperation of subject matter experts with differing backgrounds. Each discipline has its own viewpoint on the topics addressed by the collaboration team, which complicates multi-disciplinary work. Software tools often embed these differences, rendering the exchange of data subject to the assumptions of the differing backgrounds and disciplines. This can pose significant risk to well, human, asset value and environmental safety. Well construction software applications need to be versatile enough to describe and interpret the meaning of exchanged data while allowing for the inherent assumptions.

Facts about data best describe the meaning of the exchanged data. A semantic network can describe the relationships between concepts or meanings, which it does by collecting and relating facts about the data. The existing data repositories used in well construction use predefined and agreed upon meanings for the contained data. A semantic network, however, allows software applications to interpret the meaning of the transferred data, even when the original meaning and inherent assumptions are different from that of the receiving application. Obviously, the manual generation of such semantic information by end-users is not practical. Each software application needs to generate and interpret semantic information alongside the published data.

A simple example illustrates the potentially costly consequences of the differing meanings of exchanged information in a multi-disciplinary well construction environment. Three different end-users—a geomechanics engineer, a drilling engineer, and a measurement-while-drilling (MWD) engineer—exchange information, using computer applications, about downhole pressure measurements acquired during a drilling operation. The downhole pressures are in equivalent mud weight (EMW) units. Due to the different disciplines involved, each application utilizes a different definition when converting downhole pressure to EMW. The assumptions related to the accuracy of not only the mud density but also the vertical depth, then create expectations that may not be in line with reality, and each of the three different users themselves have different uncertainty expectations.

These different definitions require information on context to avoid misinterpretation. If the three applications can document the meaning of the exchanged data and can interpret the meaning of the received semantical information, then they can mitigate the risk of incorrect interpretation of the EMW data and provide quantification of the associated uncertainties. The scenario described also covers a change in context from conventional drilling to a well control incident. A different interpretation of the meaning of the data must follow the change in context. The paper explains how the three applications can describe the meaning of the exchanged data in order to correctly interpret information and manage the associated uncertainties both in terms of expectations as well as results.

Digitalization of the drilling industry is progressing rapidly, accompanied by the ability to exchange information between computer systems. This clearly breaks down the walls between the different disciplines involved in well planning and drilling operations. It is also important to recognize the uncertainty expectations between disciplines. These expectations must be managed so that the assumptions made are harmonious. To maximize potential opportunities, it is important to maintain the quality of information exchanged between computer systems to mitigate the introduction of significant risk to well, human and environmental safety.

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