Drilling Systems Design and Operational Management: Leveraging the Value of Advanced Data-Driven Analytics

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1 Drilling Systems Design and Operational Management: Leveraging the Value of Advanced Data-Driven Analytics O. Bello, T. Yaqoob, C.H. Udo, J. Oppelt, J. Holzmann, A. Asgharzadeh, O.G. Meza, M. Spinneker Clausthal University of Technology, Germany

2 CONTENT Motivation Big Data and Analytics Overview Big Data and Analytics in Oil and Gas Big Data Analytics in Drilling Operations and Management System: Promise and Potential Pilot Study Key Take-Aways

3 Motivation

4 Motivation The ability to gather insights from historical data and current data can help operators/servicing companies cope in the current low oil price environment Maintaining customers satisfaction Maintain optimal production level while increasing recoverable reserves and reducing unplanned well downtime Leveraging on new technologies that apply analytics to business strategy and operations can help Operational Performance & Optimization - Expectation to achieve more with less - Big Data Analytics (data-driven analytics) will change our operational workflow After Cisco

5 Big Data and Analytics Overview Big Data and Analytics Definition Analytics Big Data is involves the frontier the of ability a firm s to gain ability insight to store, from process data by and applying access statistics, all the data mathematics, it needs to simulations, operate effectively, optimizations make or decisions, other techniques reduce to risks help and a business serve customers make a decision Forrester about an issue or opportunity that needs to be addressed Big Data isn't about the size of your data set, it's about what you do with the data you already have Big Data is the data characterized by 3 attributes: volume, velocity and variety IBM Big Data is the data characterized by the 4 key attributes: volume, variety, velocity and value Oracle Big Data Analytics Objective

6 Big Data and Analytics Overview Big Data and Analytics: Characteristics and Types 1. Prescriptive Analytics Using data to suggest the optimal solution. This is commonly associated with optimization 2. Predictive Analytics Using data to predict trends and patterns. This is commonly associated with statistics or data mining. Very large datasets and more of a real time VALUE E.g. weather forecast, to forecast future demand or to forecast the price of fuel, oil production, etc 3. Descriptive Analytics Using historical data to describe the business. This is usually associated with Business Intelligence (BI) or Visibility Systems (VS). E.g. use of descriptive analytics to better understand your historical patterns

7 Big Data and Analytics Overview After IBM Why is Big Data and Analytics Important? Key Company Benefits of: Faster, smarter and better decision making (strategic and operational) Foundation for scaled processes, insight and analysis Exploring new opportunities and mitigating risks It allows to search and analyze all data of any type and of any size with much agility Key Techniques for Data Analytics: Data Management Predictive Analytics Data Mining Data Cleaning and Storage

8 Big Data and Analytics Overview Five Keys to succeeding with Big Data and Analytics Understand the possibilities Combining hundreds of data elements and terabytes of data does not automatically produce results Tap into IT systems that manage data and provide deep analysis There s typically no single tool or approach that addresses an organization s need Build workflows and policies that facilitate the use of big An organization must define ownership and how to different groups can access and use data Focus on security and privacy concerns Find the needed talent to put big data to work

9 Big Data and Analytics in Oil and Gas Application of BDA in oil and gas industry is in the experimental stage Only few companies have adopted BDA in their activities - exploration, development and production phases o o o o o o o Seismic data processing Reservoir modelling and simulation Geological interpretation Production enhancement and optimize oil recovery from existing wells Reservoir identification Asset Monitoring Equipment maintenance After University of Granada research group

10 BDA in Drilling-Completion Operations and Management System: Promise and Potential Volume ROP, Sensors, Flow, Pressure Variety Final well report, daily drilling report Velocity Real time data acquisition, mud logging/lwd/mwd Variability Data Interpretation and Optimization

11 BDA in Drilling Operations and Management System: Promise and Potential Value Propositions Improved drilling program ( drilling operational efficiency) Improve HSE Performance Reduce Risks Reduce NPT Reduce Costs

12 BDA in Drilling Operations & Management System: Potential Areas Alarm Drilling Management Oil System Well Cementing Operations for Drilling Performance System Operations Drilling Data-driven Analytics Drilling System Operations Performance Model-driven analytics for improving alarm management in drilling Advanced Failure computing Diagnostic methods and prognostic for knowledge operations for drilling discovery pipe connections and prognosis using oil real-time well streaming data cementing. and model-driven analytics Alarm Management for Drilling System Operations Data-driven techniques on alarm analysis and improvement in drilling Oil cementing integrity health monitoring Data-driven analytics for detecting systems by prognostive computing data driven analytics. and diagnosing drilling operational problems Data-driven analytics for drilling process data visualization and drilling Apache Drilling spark dynamics application and Optimization: of for near problem real-time Drill detection String failure Vibration detection Analysis and forecasting and Monitoring of using Real oil Time well Streaming cement subject Data and to fatigue Model-driven Analytics Oil Well Cementing Evaluation and comparison anomaly detection algorithms in streaming Predicting Development methodology of a hybrid for intelligent real-time drilling DTS system data data for analysis online real-time during oil performance well cementing using monitoring apache of spark drilling big operations data analytics Fault Real-time detection analysis in oil of well streaming cementing data Drillstring operations and development Dynamics using downhole of alarm and distributed Mechanics algorithms for Automation temperature system for cuttings sensed drilling data concentration performance assimilation estimation techniques. in the wellbore annulus

13 Pilot Study

14 Key Take-Aways Big data analytics in oil and gas industry is evolving into a promising field for providing insight from very large data sets and improving outcomes Leveraging on BDA could aid oil and gas companies track new business opportunities, improve safety, reduce costs and reengineering of drilling design and operational activities The knowledge transfer of BDA technology will not only validate use case for huge data in drilling systems design and operation management, but bring substantial value resulting to cost reduction, high failure risk mitigation, NPT reduction and improvement on both drilling and HSE performances

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17 Motivation

18 Big Data and Analytics Overview Big Data and Analytics: Challenges Privacy and security - Privacy and Security are sensitive and includes conceptual, technical as well as legal significance - Most Peoples are vulnerable to Information Theft Data access and sharing information - The data management and governance process bit complex adding the necessity to make data open and make it available to government agencies - Expecting sharing of data between companies is awkward Analytical Challenges - Analysis on such huge data, requires a large number of advance skills - Type of analysis which is needed to be done on the data depends highly on the results to be obtained Human resources and manpower

19 Big Data and Analytics Overview Why is Big Data and Analytics Important?

20 Big Data and Analytics Overview Big Data and Analytics: Characteristics and Types

21 Big Data and Analytics Overview Source: IBM Corporation

22 Pilot Study Data Collection Examine several drilling rigs technologies/alternatives Identification of most effective parameters; technical, economical and environmental that influence drilling rigs selection Database Development Design and implementation of a robust database management system (DBMS) for land rig Implementation of Artificial Intelligence (AI) Techniques Hybridization models for drilling rig analysis and selection To develop and implement a framework for optimizing selection of drilling rigs using AI (machine learning) models - FL, FTOPSIS and their hybrids Intelligent Support System Unconventional drilling rig selection optimization: Intelligent support system and automatic ranking Field application of the proposed intelligent support system