Hybrid Intelligent Decision Support System for Drill Rig Performance Analysis and Selection During Well Construction

Abstract

Drilling rig selection is one the major bottom line during the development phase of hydrocarbons or geothermal well based on comprehensive consideration of a variety of factors for its purpose. The process of drilling a well is highly cost as it involves hiring a drilling rig and crew for the duration of drilling the well. Problem of drilling rig performance and selection has always been viewed as the most important responsibility during the well design phase of a field development, this is mainly caused due to multiple rig manufacturers in the market that meets the operational conditions but not in accordance most operators design specifications. This solution has always been a complex process as various criteria, known and half known are involved in making final decision. Thus, there is a need for simple, systematic applications/tools that can be used to support the decision making considering a number of selection attributes and their interrelations in making right decisions. This study proposes a peculiar approach, an intelligent decision support application hybridizing distance based approach (DBA) and an artificial intelligence technique, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) that will assist well engineers, E&P operators in contacting multiple appropriate rig manufacturer that will deliver a reliable drilling performance resulting to safety drilling operations, mitigate effect of time delay, environmental friendly and most importantly be economically viable for different application. Currently, the conventional approach for the selection of appropriate drill rig for onshore operational activities is based on method of exclusion associated with engineering experience and lithology of the field to be developed serving as key drive factors. In order to make it scientific, both qualitative and quantitative parameters influencing drill rig selections were first established using database management system and was later implemented using both two techniques, DBA and FTOPSIS. However, for the purpose of this study, selected onshore rigs are considered for the hybrid model implementation. The novelty of this study lies in the application of a hybrid approach to a real E&P industry case. This study has dealt with one of the most important subjects as related to well design planning, providing a better decision for the selection of drill rig using appropriate quantitative techniques. This work also helps to select the best way to proceed in a situation when the outcomes are uncertain. The proposed tool will also set up a benchmarking process providing a feedback to the rig manufacturers for further improvement of their equipments that account to safety, less environmental impact, minimal NPT and better drilling performance operation.

Decision making problem solving using fuzzy networks with rule base aggregation

“…On the other hand, the reliability of decision information and the experience of experts are still in need of better incorporation into modelling complex decision-making processes [4]. For instance, how self-confident in their choices are stakeholders as decision makers and how much knowledge experts as financial analysts have in appropriate asset classes and markets [5]. Moreover, established TOPSIS methods have a very low transparency level and consequently are not able to trace the performance of benefit and cost measures [6].…”