USE CASES PUBLICATIONS BLOG

Axial Vibration Monitoring in Laboratory Scale Using CDC miniRig and Vibration Sensor Sub

Since vibration and shock loads are always known as a destructive loads while drilling a borehole and reason for tool failure, more detailed studies of vibration and dynamic behavior of drill string and bottom hole assembly to prevent any time lost and hole problem is needed to be done.

Diagnosing Drilling Problems Using Visual Analytics of Sensors Measurements

One of the major challenges in the drilling industry is the quick detection of problems that can occur during drilling a deep well due to high cost implications.

Drilling Events Detection Using Hybrid Intelligent Segmentation Algorithm

Several sensor measurements are collected from drilling rig during oil well drilling process. These measurements carry information not only about the operational states of the drilling rig but also about all higher level operations and activities performed by drilling crew. Automatic detection and classification of such drilling operations and states is considered as a big challenge in drilling industry.

Operations Recognition at Drill-Rigs

Drilling an oil & gas well is always guided by the demand to prevent crises affecting technique, investment and security.

Model-Based Hookload Monitoring and Prediction at Drilling Rigs using Neural Networks and Forward-Selection Algorithm

The use of neural networks and advanced machine learning techniques in the oil & gas industry is a growing trend in the market.

Model Based Monitoring of Wellbore Hydraulics for Abnormal Event Detection

With the increasing demand for energy in the last decades, the petroleum industry was forced to push the limits to levels that have never been reached before. Exploring very deep waters, drilling under varying conditions of extreme pressures and temperatures and dealing with issues which involve a new level of understanding, are challenges which need to be overcome in order to safely and successfully accomplish the planned goals.

Using Neural Network Technique to Study the Effect of Formation Mechanical Properties on Dynamic Behavior of Drill String

Vibrations are caused by bit and drill string interaction with formations under certain drilling conditions. They are affected by different parameters such as weight on bit, torque on bit, rotary speed, mud properties, bottom hole assembly and bit design as well as by the mechanical properties of formations.

Experimental Evaluation of Real-Time Mechanical Formation Classification using Drill String Vibration Measurements

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October 2012.

Automated Real-time Drilling Hydraulics Monitoring

This paper was prepared for presentation at the SPE/IADC Drilling Conference and Exhibition held in Amsterdam, The Netherlands, 1–3 March 2011.

Rigorous Identification of Unplanned and Invisible Lost Time for Value Added Propositions Aimed at Performance Enhancement

This paper was prepared for presentation at the SPE/IADC Drilling Conference and Exhibition held in Amsterdam, The Netherlands, 1–3 March 2011.

A Statistical Feature-Based Approach for Operations Recognition in Drilling Time Series

Recognizing patterns in time series has become a necessary machine learning task in many fields including medicine, finance, business and oil and gas industry. In this paper we propose a feature-based approach to recognize patterns in drilling time series data.

Automatic Threshold Tracking of Sensor Data Using Expectation Maximization Algorithm

In this paper we present a novel method for automatic threshold handling and tracking of sensor data at drilling rigs. A hybrid system for automated drilling operation classification is extended by the Expectation Maximization algorithm in combination with the Bayes’ theorem to find automatically threshold values required by a rule based system used in an automated drilling operations classification system.

Case Study–Field Implementation of Automated Torque-and-Drag Monitoring for Maari Field Development

This paper was prepared for presentation at the 2010 IADC/SPE Drilling Conference and Exhibition held in New Orleans, Louisiana, USA, 2–4 February 2010.

Rigorous Drilling Nonproductive-Time Determination and Elimination of Invisible Lost Time: Theory and Case Histories

This paper was prepared for presentation at the SPE Latin American & Caribbean Petroleum Engineering Conference held in Lima, Peru, 1–3 December 2010.

Automated Operations Classification using Text Mining Techniques

Abstract- Classifying textually-described drilling operations presents big challenges because drilling personnel use a specialised way of describing the drilling process as part of their daily reporting routine.

Abnormal Oil Well Drilling Operations Detection Using Smallest Principal Components

Abstract – In this paper, a novel and non-parametric method for detecting abnormal drilling situations is proposed. The concept of smallest principal components in combination with the Mahalanobis Distance from multivariate Gaussian distribution is used to detect abnormal drilling situations compared to normal drilling operations.

Case Study–Field Implementation of Automated Torque-and-Drag Monitoring for Maari Field Development

This paper was prepared for presentation at the 2010 IADC/SPE Drilling Conference and Exhibition held in New Orleans, Louisiana, USA, 2–4 February 2010.

Case History: Automated Drilling Performance Measurement of Crews and

This paper was prepared for presentation at the SPE/IADC Drilling Conference and Exhibition held in Amsterdam, The Netherlands, 17–19 March 2009.