TY - BOOK

T1 - Decision Support System for Drilling Process Optimization

AU - Mathis, Wolfgang

N1 - no embargo

PY - 2007

Y1 - 2007

N2 - This work introduces a decision support system for oil and gas-well drilling process optimization. The time spent to drill a well is typically longer than what is technically possible due to down times. Identified lost time consists of incidents which are documented in daily drilling reports. The invisible lost time is defined as time losses, when operations are not running at the technical limit. Unfortunately, human-reported operations are not exact enough, to identify the invisible lost time. To overcome these limitations, an expert system is applied to derive a highly detailed process description, which is used to replace the coarse human-reported operation breakdown. The necessary input parameters are sensor measurements regularly recorded for more than a decade. This provides a huge repository to derive benchmarks. To enable a timely and successful decision making process, a 5-step data management strategy is introduced. The system is highly automated, to reduce human interference as much as possible. Prior to the operation detection, special attention is given to data acquisition and data quality control. An expert systems is applied to replace human observations and Online Analytical Processing (OLAP) supports the data interpretation. Furthermore, data visualization techniques are discussed, to support the human analyst in his decision making process. Finally, practical examples are presented, which prove the efficiency of the introduced system.

AB - This work introduces a decision support system for oil and gas-well drilling process optimization. The time spent to drill a well is typically longer than what is technically possible due to down times. Identified lost time consists of incidents which are documented in daily drilling reports. The invisible lost time is defined as time losses, when operations are not running at the technical limit. Unfortunately, human-reported operations are not exact enough, to identify the invisible lost time. To overcome these limitations, an expert system is applied to derive a highly detailed process description, which is used to replace the coarse human-reported operation breakdown. The necessary input parameters are sensor measurements regularly recorded for more than a decade. This provides a huge repository to derive benchmarks. To enable a timely and successful decision making process, a 5-step data management strategy is introduced. The system is highly automated, to reduce human interference as much as possible. Prior to the operation detection, special attention is given to data acquisition and data quality control. An expert systems is applied to replace human observations and Online Analytical Processing (OLAP) supports the data interpretation. Furthermore, data visualization techniques are discussed, to support the human analyst in his decision making process. Finally, practical examples are presented, which prove the efficiency of the introduced system.

KW - Drilling

KW - Process Optimization

KW - Event Operation

KW - Recognition

KW - Expert System

KW - Artificial Intelligence

KW - Quality control

KW - Visualization

KW - Prozess Optimierung

KW - Bohrung

KW - Experten System

KW - künstliche Intelligenz

KW - Qualitätskontrolle

KW - Visualisierung

M3 - Doctoral Thesis

ER -