Change Management Challenges Deploying a Rig-Based Drilling Advisory System

M. Behounek, B. Millican, B. Nelson, M. Wicks, E. Rintala, M. White, T. Thetford, P. Ashok, D. Ramos: “Change Management Challenges Deploying a Rig-Based Drilling Advisory System,” paper SPE-194184-MS, presented at SPE/IADC International Drilling Conference and Exhibition, 5-7 March, The Hague, Netherlands, 2019.

The system used for this paper consists of a Rig-based Drilling Advisory System (RDAS) where new advisory information is displayed in the driller’s cabin running real-time pattern recognition algorithms to detect drilling dysfunctions. When a drilling dysfunction is encountered, a change in drilling parameters is suggested. Additionally, drilling parameters from offset wells are made automatically available for the driller’s use on the drilling screen. Through this process, we are entrusting the field personnel with a slightly higher level of technical responsibility. The team iteratively improved the system using feedback from drillers who used the RDAS.

Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study

M. Shahri, T. Wilson, Taylor Thetford, B. Nelson, M. Behounek, A. Ambrus, J. D’Angelo, P Ashok: “Implementation of a Fully Automated Real-Time Torque and Drag Model for Improving Drilling Performance: Case Study,” paper SPE-191426-MS, presented at the SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, 2018.

The drilling industry has made significant progress on physics-based torque and drag (T&D) models that can run either offline (pre-job or post-job) or in real-time. Despite its numerous benefits, real-time T&D analysis is not prevalent since it requires merging real-time and contextual data of dissimilar frequency and quality, along with repeated calibration whose results are not easily accessible to the user. Our goal is to implement a rig-based T&D advisory system which overcomes these obstacles.

Adopting Physical Models in Real-Time Drilling Application: Wellbore Hydraulics

A. Ambrus, P. Ashok, D. Ramos, A. Chintapalli, A. Susich, T. Thetford, B. Nelson, M. Shahri, J. McNab, M. Behounek: “Adopting Physical Models in Real-Time Drilling Application: Wellbore Hydraulics,” paper SPE-191797-MS, presented at the SPE Liquids-Rich Basins Conference – North America, 5-6 September, Midland, Texas, 2018.

The first step towards drilling optimization and automation is a reliable data acquisition and handling system. This includes receiving and processing different frequency data across multiple platforms and ensuring proper data quality. Once we implement such a platform, we can build different advisory solutions to improve drilling efficiency and move towards drilling automation. The developed Drilling Intelligence Guide (DIG) already enabled us to achieve the aforementioned goal and access different data (from contextual to high frequency) in real-time.

Self-Learning Probabilistic Detection and Alerting of Drillstring Washout and Pump Failure Incidents During Drilling Operations

Ambrus A, Ashok P, Ramos D, Chintapalli A, Susich A, Thetford T, Nelson B, Shahri M McNab J and Behounek M: “Self-Learning Probabilistic Detection and Alerting of Drillstring Washout and Pump Failure Incidents During Drilling Operations,” paper IADC/SPE 189700, presented at the IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, March 6–8, 2018.

Recently, Intellicess applied unique beliefs-based early detection and alerting system to drillstring washout and pump failure detection during drilling. The methodology was used to focus primarily on the time signatures of real-time and modeled pump pressure in relation to flow rate trends. Together these parameters described the status of the equipment, which was then assessed through real-time alerts. Case histories presented demonstrate that through continuous model improvements and validation, the Intellicess system was able to detect the warning signs of washout and pump failure hours before the problem was detected at the rig site. As a consequence, significant value was added through early detection of mechanical failures that allowed the driller to significantly reduce non productive time caused by pump downtime, tripping, and fishing.

A Novel Probabilistic Rig Based Drilling Optimization Index to Improve Drilling Performance

Ambrus A, Ashok P, Chintapalli A, Ramos D, Behounek M, Thetford TS and Nelson B: “A Novel Probabilistic Rig Based Drilling Optimization Index to Improve Drilling Performance,” paper SPE 186166, presented at the SPE Offshore Europe Conference and Exhibition, Aberdeen, Scotland, UK, 5-8 September 5-8, 2017. (DOI: 10.2118)

Currently, because available drilling data is uncertain and humans cannot aggregate and act appropriately on multiple data streams in real time, adjustments to drilling parameters are based primarily on experience. This paper describes a robust decision-support tool that uses a Bayesian network to represent the drilling process to overcome these limitations, The new system automatically aggregates these data, identifies drilling inefficiencies and suggests optimal drilling parameters. The model is updated in real-time and tracks variations in drilling conditions. (more…)

Human Factors Engineering in the Design and Deployment of a Novel Data Aggregation and Distribution System for Drilling Operations

Behounek M, Thetford T, Yang L, Hofer E, White M, Ashok P, Ambrus A and Ramos D: “Human Factors Engineering in the Design and Deployment of a Novel Data Aggregation and Distribution System for Drilling Operations,” paper SPE 184743, presented at the SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, March 14–16, 2017

Automated monitoring software only adds value when end users follow up with actions based on the information and knowledge derived from it. Within the drilling process, such follow through is dependent on a few key personnel on and off the rig. To reduce the dependence on those few individuals, engineers have designed a data aggregation and distribution system that promotes proper action and leads to better performance by methodically determining the who, what, when, why, where and how for disseminating results of a real-time data analysis module. This effort is focused on understanding how the human-computer boundary works with the aim of maximizing the probability that humans will relate to, understand and take action on computer-generated information.

A Step by Step Approach to Improving Data Quality in Drilling Operations: Field Trials in North America

Ashok P, Ambrus A, Ramos D, Lutteringer J, Behounek M, Yang YL, Thetford T and Weaver T: “A Step by Step Approach to Improving Data Quality in Drilling Operations: Field Trials in North America,” paper SPE 181076., presented at the SPE Intelligent Energy International Conference and Exhibition, Aberdeen, Scotland, UK, September 6–8, 2016.

Multiple literature studies have indicated that a significant amount of data collected during drilling operations is unreliable. To move towards better data quality, two critical hurdles need to be overcome. First, the case for the value of good data needs to be made, so that resources can be allocated towards improving data quality. Second, a process needs to be established within the operator company to measure and improve the quality of data. This paper is a case study in addressing these challengesMultiple literature studies have indicated that significant amounts of data collected during drilling operations is unreliable. To move towards better data quality, the economic case for the value of good data needs to be made and a process established within operating companies to measure and improve the quality of data. In this work, we focus on eight core surface sensor measurements essential to drilling operations and attempt to assess and improve their quality. The goal is to establish a data quality improvement loop that continually accesses data, identifies issues, and implements corrective actions. This paper explains this process and how it has been applied to improve the quality of drilling data.

Automatic Sensor Data Validation: Improving the Quality and Reliability of Rig Data

Ashok P, van Oort E and Ambrus A: “Automatic Sensor Data Validation: Improving the Quality and Reliability of Rig Data,” paper SPE-163726, presented at the SPE Digital Energy Conference, The Woodlands, Texas, March 5–7.

The hostile and uncertain environment in which sensors are used and the low priority typically given to regular calibration of these sensors are the primary reasons for poor quality drilling data. Today, online streaming of sensor data from primary sensors on a rig to a real-time data monitoring facility, and software to analyze this data in real-time and to detect trends and identify potential drilling problems long before they occur are all becoming commonplace. However, these applications require good quality data to perform accurate analysis and when data is not validated, false and missed alarms are the norm and cause supposedly autonomous software applications to require continuous human supervision. In this paper, a novel technique for real-time sensor data validation is proposed that improves the quality of data collected from the sensors on a rig.