Hydraulic Monitoring and Well-Control-Event Detection With Model-Based Analysis

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 24803, ’Hydraulic Monitoring and Well-Control-Event Detection by Use of Model-Based Analysis,’ by D. Todorov and G. Thonhauser, Thonhauser Data Engineering, prepared for the 2014 Offshore Technology Conference Asia, Kuala Lumpur, 25-28 March. The paper has not been peer reviewed. The major challenge in hydraulic modeling is that achieving a realistic representation of wellbore conditions is difficult in a mathematical model. This paper presents a new approach in monitoring the hydraulic system and in the recognition of well-control events at an early stage such that proper counteractions can be initiated before any damage occurs. A hydraulic real-time monitor based on sensor data has been developed, using artificial neural networks to compute, recognize, and predict abnormal events in the wellbore. Introduction Precise knowledge of hydraulic pressure while drilling is crucial for the success of the entire drilling process. The pump must operate within an accurate pressure range, providing the desired flow rate necessary for hole cleaning. The operating pressure is thereby concurrently limited by pipe and equipment constraints. Pore pressure, formation-fracture gradients, and equivalent circulating density (ECD) introduce additional complexity to the hydraulic system. However, when taking the complete drilling system under consideration, one must remember that severe challenges arise in representing the reality because of the restricted knowledge of several operational parameters at any point of time. To improve the quality and reduce the influence of the detailed input parameters, the deterministic approach is improved by introducing numerical methods that add significantly to the performance of the system. The hybrid simulator is then in fact able to handle the uncertainties regarding the input data. Furthermore, a major step toward gaining optimal results is achieved by the implementation of automatic-drilling- operation recognition systems. These enhance the output of the numerical models and provide the means for operation-based monitoring and performance analysis.