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Probabilistic Drilling-Optimization Index Guides Drillers To Improve Performance

This paper proposes a metric for quantifying drilling efficiency and drilling optimization that is computed by use of a Bayesian network. The network combines the identification of drilling dysfunctions (i.e., vibrational modes), autodriller dysfunctions, and mechanical-specific-energy (MSE) tracking into a single, normalized quantity that the driller can use to help decide which control parameters to adjust. The driller may be provided with operational cones on a weight-on-bit (WOB)/rotary speed plot to assist in this task.

Methodology

The method proposed in this paper combines real-time surface measurements available on a drilling rig, derived quantities such as MSE and bit aggressiveness, and formation data (e.g., rock strength) into a probabilistic framework capable of handling the inherent uncertainty in the data and the process. The measured and derived parameters are encoded into a set of probabilistic features indicative of either the location of a particular physical attribute or a trend/movement of the attribute. These features are used to infer the beliefs of various drilling dysfunctions as well as the belief of an optimal drilling condition. The end result is a drilling-optimization index calculated whenever a drilling activity occurs. Because of its holistic nature, this index factors in the presence of various dysfunctions as well as suboptimal drilling rates. Additional dysfunctions can be added to the index easily, and the Bayesian network is forgiving when some data is missing. The index can be integrated easily into a decision-support system for monitoring drilling performance and providing recommendations for improved efficiency. Fig. 1 shows a detailed flowchart of the method.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 186166, “A Novel Probabilistic Rig-Based Drilling-Optimization Index To Improve Drilling Performance,” by A. Ambrus, SPE, P. Ashok, SPE, A. Chintapalli, and D. Ramos, Intellicess, and M. Behounek, SPE, T.S. Thetford, SPE, and B. Nelson, SPE, Apache, prepared for the 2017 SPE Offshore Europe Conference and Exhibition, Aberdeen, 5–8 September. The paper has not been peer reviewed.
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Probabilistic Drilling-Optimization Index Guides Drillers To Improve Performance

01 September 2018

Volume: 70 | Issue: 9

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