Drilling Systems Design and Operational Management ... · PDF fileMotivation Opeyemi Bello • Drilling Systems Design and Operational Management: Leveraging the Value of Advanced

Drilling Systems Design and OperationalManagement: 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

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

Opeyemi Bello • Drilling Systems Design and OperationalManagement: Leveraging the Value of Advanced Data-Driven Analytics

Motivation


Motivation


Maintaining customers satisfaction

• Maintain optimal production level while increasing recoverable reserves and reducing unplanned well downtime

Operational Performance & Optimization

- Expectation to achieve more with less -

After Cisco

The ability to gather insights from historical

data and current data can help

operators/servicing companies cope in the

current low oil price environment

Leveraging on new technologies that apply

analytics to business strategy and operations

can help

………Big Data Analytics (data-driven analytics) will change our operational workflow



Big Data and Analytics Definition

➢ Big Data is the frontier of a firm’s ability to store, process and

access all the data it needs to operate effectively, make decisions,

reduce risks and serve customers – Forrester

❖ 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 isn't about the size of your data set, it's

about what you do with the data you already have

“Analytics” involves the ability to gain insight from data by

applying statistics, mathematics, simulations, optimizations or other

techniques to help a business make a decision about an issue or

opportunity that needs to be addressed

Big Data Analytics Objective



Big Data and Analytics: Characteristics and Types

VALUE

1. Prescriptive Analytics – Using data tosuggest the optimal solution. This iscommonly associated with optimization

2. Predictive Analytics – Using data topredict trends and patterns. This iscommonly associated with statistics ordata mining. Very large datasets andmore of a real time E.g. weatherforecast, to forecast future demand or toforecast the price of fuel, oil production,etc

3. Descriptive Analytics – Using historicaldata to describe the business. This isusually associated with BusinessIntelligence (BI) or Visibility Systems (VS).E.g. use of descriptive analytics to betterunderstand your historical patterns



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

After IBM


❖ 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


Five Keys to succeeding with Big Data and Analytics

Big Data and Analytics in Oil and Gas


Only few companies have adopted BDA in

their activities - exploration, development

and production phases

o Seismic data processing

o Reservoir modelling and simulation

o Geological interpretation

o Production enhancement and optimize oil

recovery from existing wells

o Reservoir identification

o Asset Monitoring

o Equipment maintenance

After University of Granada research group

Application of BDA in oil and gas industry is

in the experimental stage

BDA in Drilling-Completion Operations and Management System: Promise and Potential


• ROP, Sensors, Flow, Pressure

Volume

• Final well report, daily drilling report

Variety

• Real time data acquisition, mud logging/LWD/MWD

Velocity

• Data Interpretation and Optimization

Variability

Improved drilling program ( drilling operational efficiency)

Reduce NPT

Reduce Risks

Reduce Costs

Improve HSE Performance

BDA in Drilling Operations and Management System: Promise and Potential

Value Propositions

BDA in Drilling Operations & Management System: Potential Areas


Drilling Data-driven Analytics

Drilling System Operations Performance

Alarm Management for Drilling System Operations

Oil Well Cementing

Drillstring Dynamics and Mechanics

Drilling System Operations Performance

Failure Diagnostic and prognostic for drilling pipe connections using real-time streaming data and model-driven analytics

Data-driven analytics for detecting and diagnosing drilling operational problems

Drilling dynamics and Optimization: Drill String Vibration Analysis and Monitoring using Real Time Streaming Data and Model-driven Analytics

Development of a hybrid intelligent system for online real-time performance monitoring of drilling operations

Automation system for cuttings concentration estimation in the wellbore annulus

Alarm Management for Drilling System Operations

Model-driven analytics for improving alarm management in drilling operations

Data-driven techniques on alarm analysis and improvement in drilling systems

Data-driven analytics for drilling process data visualization and drilling problem detection

Evaluation and comparison anomaly detection algorithms in streaming drilling data

Real-time analysis of streaming data and development of alarm algorithms for drilling performance

Oil Well Cementing

Advanced computing methods for knowledge discovery and prognosis in oil well cementing.

Oil cementing integrity health monitoring system by prognostive computing data driven analytics.

Apache spark application of for near real-time failure detection and forecasting of oil well cement subject to fatigue

Predicting methodology for real-time DTS data analysis during oil well cementing using apache spark big data analytics

Fault detection in oil well cementing operations using downhole distributed temperature sensed data assimilation techniques.

Pilot Study


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

Motivation




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



Why is Big Data and Analytics Important?



Big Data and Analytics: Characteristics and Types



Source: IBM Corporation

Pilot Study


• Examine several drilling rigs technologies/alternatives

• Identification of most effective parameters; technical, economical and environmental that influence drilling rigs selection

Data Collection

• Design and implementation of a robust database management system (DBMS) for land rig

Database Development

• To develop and implement a framework for optimizing selection of drilling rigs using AI (machine learning) models - FL, FTOPSIS and their hybrids

Implementation of Artificial Intelligence (AI) Techniques

Hybridization models for drilling rig analysis and selection

• Unconventional drilling rig selection optimization: Intelligent support system and automatic ranking

• Field application of the proposed intelligent support system

Intelligent Support System

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Drilling Systems Design and Operational Management ... · PDF fileMotivation Opeyemi Bello • Drilling Systems Design and Operational Management: Leveraging the Value of Advanced