Integrating Data Mining and Expert Knowledge for an Artificial-Lift Advisory System
Authors: Bybee Karen 
Published: February 2015

This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 128636, ’Integrat ing Data Mining and Expert Knowledge for an Artificial Lift Advisory System,’ by E. De la Vega, G. Sandoval, and M. Garcia, SPE, Pemex, and G. Nunez, SPE, A. Al-Kinani, SPE, R.W. Holy, SPE, H. Escalona, SPE, and M. Mota, Schlumberger, originally prepared for the 2010 SPE Intelligent Energy Conference and Exhibition, Utrecht, The Netherlands, 23-25 March. The paper has not been peer reviewed.

The full-length paper describes a new workflow to accelerate and improve decisions regarding where and when to apply an artificial-lift system in fields with a considerable number of active wells. The workflow deploys a hybrid combination of a user-driven expert system and a data-driven knowledge-capturing system calibrated with historical data. The two systems interact to determine the right point in time to support a particular well with an artificial-lift system.  

Introduction

Typically, asset expertise is stored in spreadsheets that define the limits for condition parameters within which a specific artificial-lift system needs to be installed. As a first step toward decision automation, these limits are implemented in a rule-based reasoning system deploying if-then-else logic to automate the knowledge. After successful validation of the rule-based approach, the information about the limits and the combination and interaction of the parameters is fed into a Bayesian belief network (BBN) capable of performing a more robust reasoning process under uncertainty or missing parameters. The setup process of the BBN requires frequent interaction with the asset team to ensure the validity and consistency of the system recommendations. While the iterative validation is necessary for the BBN setup, it also enables the asset team to review and visualize their complex decision-making process thoroughly and helps to define more-efficient decision logic.

In parallel, a data-mining analysis on historical data is performed to find the decision logic as it was applied in the field. The data-mining study incorporates operational data (e.g., tubinghead pressures, line pressures, and calculated gas rates), well-test data, and information from well-intervention reports. The objectives of the data-mining analysis are:

To identify patterns in operational and well-test data that lead to the decision of a new artificial-lift system.

To identify potential correlations of well-intervention activity and incremental production after intervention to quantify the success of a specific artificial-lift installation under specific conditions.

Case Study

Activo Integral Burgos (AIB) is a typical example of a large gas field where production declined as a result of gas-loading backpressure and reduced permeability in the target formation. The fast decline of the gas wells during their first year of production drove a change from a reactive to a proactive management approach to monitor the field and select candidates for workovers and artificial-lift systems. However, the large number of wells in AIB (approximately 3,500 active wells) and the fact that 95% of the data is obtained by manual surveillance suggest the need for some level of automation to aid decision making.