Securing and Enabling Data Driven Oil Fields: A Look at the Key Drivers and Critical Success Factors of Operationalizing Digital Oil Fields

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1 Securing and Enabling Data Driven Oil Fields: A Look at the Key Drivers and Critical Success Factors of Operationalizing Digital Oil Fields Whitepaper April 2015 Presented to: Big Data Analytics For Oil and Gas Conference Abu Dhabi Booz Allen Hamilton Inc. Suite 1801, Etihad Tower #3 Abu Dhabi, United Arab Emirates

2 Table of Contents Executive Summary... 1 Introduction... 1 Key Drivers... 3 Production Optimization... 3 Labor Utilization... 4 HSSE Enhancements... 4 Implementing Data Driven Oil Fields... 5 Security... 5 Integration... 6 Analytics... 7 Conclusion... 8 About the Authors... 8 About Booz Allen Hamilton List of Figures Figure 1: Important Developments in Data Driven Oil Fields... 2 Figure 2 and 3: Reliance on Aging Giant Fields... 3 Figure 4: Industry Labor Shortage... 4 Figure 5: Percent of Industry Fatalities Associated With Each Reporting Category... 4 Figure 6: Booz Allen Hamilton ICS Cybersecurity by Design throughout the EPC Life Cycle... 5 Figure 7: Booz Allen Hamilton s Approach to Helping Oil and Gas Companies Achieve Information Dominance... 6 Figure 8: Booz Allen Hamilton s Cloud Analytics Reference Architecture... 7 Figure 9: Sample Benefits of DDOFs to Oil and Gas Companies... 8 Figures 10 and 11: Illustration of Improvements Within Existing Work Streams and Target State Connectivity Across Departments... 9

3 Executive Summary Technological advancements in the Oil and Gas (O&G) industry have allowed for the execution of increasingly complex projects. These projects enable the rapid and reliable recovery of hydrocarbons in even the most challenging environments. However, the increasing complexity of oil recovery comes with increasing costs. These rising costs and the volatility of oil prices have placed increasing pressure on O&G companies to optimize their production. Additionally, given the industry wide labor shortage, companies are struggling to capture the institutional knowledge of senior staff, and maximize the utilization of their employees. Finally, Health, Safety, Security & Environment (HSSE) are ever present concerns in the industry. As operating environments become more complex and regulations become more stringent, companies will need to remain vigilant in their efforts to produce hydrocarbons in the safest most eco-friendly way possible. Thus, to meet all of these challenges, organizations will need to move towards Data Driven Oil Fields (DDOFs). O&G companies are producing enormous amounts of data; however, there is a shortage of capabilities that enable these organizations to use the data generated to drive business decisions. There are three critical components that organizations must implement to fully realize these capabilities: security, integration, and analytics. Organizations must implement security up front and by design in order to ensure the integrity of both their data and operations. Next, organizations must integrate their infrastructure, data, and processes within and across departments. Finally, to effectively generate insights to drive the business, there must be a robust analytic platform that enables users to ask intelligent questions of the underlying data. Introduction Since the 1960s, the Oil and Gas industry has experienced a large number of technological advances (Figure 1), which have increased the amount of data available to companies. These advances include satellite transmission of logging data, down-hole pressure and temperature gauges, on demand data delivery, web-based monitoring of Electrical Submersible Pumps (ESP), remote operation centers, real-time fracturing control, and much more. 1

4 Figure 1 Important Developments in Data Driven Oil Fields 1 First permanent pressure and temperature (P/T) gauge installed First transmission of onshore down hole gauge data to surface On demand data delivery made possible Schlumberger s first remote operational support center (OSC) World water depth record is set by drilling in 3107 meters of water First logging data transmission via satellite Quartz crystal permanent down hole P/T gauge fitted to subsea well Online gas optimization introduce Web-based monitoring of electric submersible pumps (ESPs) Real-time fracturing control 2015 Crude prices drop below $50/bbl While all these technological advancements have helped to generate a rich array of data, it is estimated that O&G companies only use one percent of their data. Given the many ways processes can be improved with effective data use, companies that aren t making effective use of their data are leaving money on the table. In fact, it s estimated that the industry could see a 6- to 8-percent increase in production via the implementation of DDOFs. 2 Effective data integration and management can also help reduce downtime, thereby reducing costs. For example, in offshore environments companies can expect to see as much as a 13-percent increase in uptime. 2 Lastly, effective use of data has become a competitive necessity, with companies already seeing real benefits to their bottom line. Chevron s i-fields project, for example, is set to save the company over $1 billion annually. It is against this backdrop that the discussion of digital oil fields continues to gain prominence. However, it is not sufficient to digitize data. The data must drive actionable insights and inform business decisions if companies are to benefit. Thus traditional oil fields must evolve to DDOFs. 1 Oil Field Technology: Closer to the Digital Oil Field

5 Key Drivers There are three primary drivers to moving an organization to Data Driven Oil Fields: production optimization; labor utilization; and Health, Safety, Security and Environment (HSSE) enhancements. First, due to the aging of existing fields, and new fields becoming harder to find, production optimization is no longer a luxury. Second, the industry is experiencing a loss of institutional knowledge and a shortage of labor due to the number of experienced employees retiring and not being sufficiently replaced. Finally, HSSE enhancements are consistently a top priority in the industry with a constant drive to monitor and reduce risks associated with operations. Production Optimization The majority of the world s production comes from giant fields, most of which are in decline, making optimization a necessity. While giant fields only make up 1 percent of the total number of oil fields, they account for 60 percent of the total production. The majority of these giant fields are more than 50 years old and are in decline. Specifically, the average decline rate of the older OPEC giants is 6.5 percent. New oil is becoming harder to find and require drilling of more complex wells such as: highly deviated, multilateral, extended reach, and high-pressure/high-temp wells. Additionally, companies are increasingly focusing efforts on producing greater volumes from existing reservoirs using artificial lift, fracking, and enhanced oil recovery (EOR). These techniques are more costly and carry greater uncertainty, thus making the efficient use of data all the more critical. Figure 2 3 and 3 4 Reliance on Aging Giant Fields Global Oil Production Giant Fields vs. Normal Fields Historical Discovery of Giant Fields 40% 60% Original 2P reserves, Bboe Condensate Gas Oil Oil giants Gas giants Giant fields Giant Fields Normal Fields Oil and Gas Journal: Giant Fields Retain Dominance In Reserves Growth Uppsala University, Global Energy Systems: Giant Oil Field Decline Rates And Their Influence On World Oil Production 3

6 Labor Utilization Another important driver of Data Driven Oil Fields is improved labor utilization. Worldwide, skill shortages were by far the biggest concern of oil and gas employers. So much so, that 75 percent of respondents from international oil companies said staffing difficulties could trigger project delays, and 59 percent of respondents said that these shortages could lead to more risk-taking. 5 This shortage, caused by a combination of historically weak recruitment and top talent preferring other industries, has caused a gap of nearly 20 percent of the talent pool (Figure 4). 6 Figure 4 Industry Labor Shortage Percentage of Total Workforce Shortage of Experienced Labor The Big Crew Change Employee Age HSSE Enhancements Lastly, HSSE remains a constant concern for the industry. Specifically, organizations are always looking to reduce Loss Time Incidents, injuries and fatalities. With nearly half of all fatalities in the Oil and Gas industry associated with explosions and pressure release (Figure 5), 7 advanced monitoring and maintenance techniques could have a major impact. Predictive maintenance, people tracking, and disaster response are just a few ways Data Driven Oil Fields can help improve a company s HSSE performance. Figure 5 Percent of Industry Fatalities Associated With Each Reporting Category Other Assault or violent act 7% Exposure electricalectrical Falls from height 3% 3% 3% Struck by 16% 44% Explosions or burns 18% Caught in, under or between 5% Pressure release 5 Financial Times: Working in Global Energy 6 SBC 2010 & 2013: Oil and Gas HR Benchmark 7 OGP Data Series: Safety Performance Indicators

7 Implementing Data Driven Oil Fields There are three components to implementing DDOFs: security, integration, and analytics. Security The first thing an O&G company must consider when looking to develop its DDOFs capabilities is how to secure both the Information Technology and Operational Technology (IT and & OT) environments. Traditionally, industrial control systems (ICS) were mistakenly thought to be secure because they were air-gapped and used proprietary technologies and protocols. In today s environment more sophisticated attacks, and increased connectivity between industrial and corporate networks have increased systems vulnerability. The oil and gas industry is particularly at risk to cyber-attacks given its criticality in the GCC, the geopolitical implications of an attack, and the instability of oil prices. As companies migrate to more integrated environments the attack area increases and so does the organizations vulnerability. Thus companies must implement a defense-in-depth model of cyber security. This requires security to be designed into every stage of the project lifecycle (Figure 6). Doing so requires experienced professionals who are capable of carrying out risk and vulnerability assessments, identifying gaps, and proposing solutions. These solutions should include detailed network architecture designs, engineering specifications, and a Security Operation Center (SOC). The SOC should leverage machine-learning algorithms to baseline the organization and detect statistical anomalies. Implementing these capabilities allows the organization to develop a predictive security posture, and will help them address issues before they occur. Figure 6 Booz Allen Hamilton ICS Cybersecurity by Design throughout the EPC Life Cycle EPC Project Life Cycle Conceptual Design Initial Specifications Front-end Engineering Detailed Engineering Procurement Construction Commissioning Handover FAT ifat SAT Review & Monitoring ICS Cybersecurity Activities Definition of ICS Cybersecurity Requirements and Development of Policies, Plans, and Procedures Secure Architecture & Network Design Detailed Cybersecurity Design Review and Assessment Procurement Controls Development and Vendor Evaluation Comprehensive Technical Security Evaluation including Penetration Testing Verification and Validation Testing of Implemented Security Controls Cybersecurity Education, Training, and Awareness Continuous Monitoring, Compliance Evaluation, and Security Operations Planning Example 1: One application for the predictive capabilities of the SOC is real-time monitoring of rotating equipment on the rig. Using statistical analysis of sensor data from these types of equipment the operator can get alerts whenever performance deviates from normal. This alert would then trigger an investigation allowing operators to preemptively introduce mitigation efforts, preventing costly downtime. 5

8 Integration Next, to make the most use of the information available to them, O&G companies must think holistically about integration including the integration of data, infrastructure and processes. In terms of data, companies ought to integrate the various data sources such as geological data, logs, reports, configurations, manuals, and open source data (Figure 7). With respect to infrastructure, companies ought to integrate the IT and OT environments and minimize the number interfaces that operators have to deal with. Finally, companies should integrate processes and workflows across the organizations to more readily capture dependencies and constraints. The net impact of full integration is a more effective organization, and a reduction of inefficiencies. Figure 7 Booz Allen Hamilton s Approach to Helping Oil and Gas Companies Achieve Information Dominance Oil Company Data Acquisition Geological Data Seismic Data Core Samples Enables Effective Operations and Decision Making Intelligence Products Drilling Mechanics Logs Formation Evaluation Rig Sensors Metering Data Work Permits HSE Reports Equipment Configuration Plant Sensor Data Physical Cybersecurity Access Logs Logs Reports Morning Reports IADC reports Configurations and Manuals Operations Reference Manual Open Data Control Systems Driving Financial Reports Meeting Minutes Maintenance Manual Global Statistics OPEC News Activities Enrich Integrate and transform data Methods Descriptive statistics Filtering Aggregation Activities Reveal trends Identify correlations Learn patterns Methods Unsupervised learning Clustering Outlier detection Activities Classify signals Predict risks Forecast resource s Methods Random forest Neural networks Bayesian analysis Collaborative filtering Activities Optimize resources Simulate decision outcomes Methods Genetic algorithms Integer programming Non-linear programming Discrete event simulation Acquisition, aggregation and enrichment of information from multiple entry points will help create a holistic view that can enhance operations and provide powerful insight Reports Dashboards Mitigations Allows for accurate analysis of trends across the organization against defined KPIs Supports strategic C- Suite decision making Reveals operational risks and potential bottlenecks in real-time Supports critical information infrastructure protection efforts by early detection of vulnerabilities Example 2: Let us assume an oil company is contemplating drilling a complex well in a difficult formation, with multiple very narrow production zones. The company is using a new drilling contractor and wants to specify the drilling parameters, the correct mud weight, and the hole cleaning schedule to minimize the chances of getting stuck. Finally, the company wants to ensure the maximum rate of penetration (ROP). Typically, getting all of the parameters right will be a combination of timely tests (e.g., FIT, LOT, FPWD), and trial and error (e.g. adjusting torque and weight on bit in real-time). The planning phases would require multiple human resources from a number of departments (including drilling engineering and reservoir management) to determine well geometry, the Bottom Hole Assembly, and optimal perforation programs However, by automating the analysis of drilling mechanic and formation evaluation logs, IADC reports, and the daily reports of the service companies, the O&G company can leverage the computational power of computers to quickly discover which previously drilled wells had the best performance, what were the characteristics of those wells, and generate recommendations for the best course of action along any desired dimension. Additionally, real-time monitoring capabilities will allow operators to make adjustment to drilling parameters as needed. 6

9 Analytics The third and final step of operationalizing DDOF is analyzing the data. Without a proper platform to analyze the data, organizations will be unable to unlock their full potential. In order to handle the full range of questions that users may wish to ask of their organization, the analytic framework should be able to dynamically transform the underlying data. This dynamic transformation will enable users to slice the data and extract only the pieces that are relevant to them. It also allows for dynamic correlations, enabling users from various parts of the organization to see what impact disparate changes have on their business units. Importantly, the best analytic platforms do not require the user to have a complete view of how the data will be used before they are developed. Figure 8 Illustrates Booz Allen Hamilton s Cloud Analytics Reference Architecture. In this model the data is ingested with minimal refining. The views and indexes are then created dynamically as needed, and the visualization layer clearly communicates to end users the information they are looking for. Figure 8 Booz Allen Hamilton s Cloud Analytics Reference Architecture Purpose Architecture Function Visualization Enabled by customizable interfaces and visualizations Visualization, Reporting, Dashboards, and Query Interface Services (SOA) Analytics and Services Tools for analysis, modeling, testing, and simulation Machine Learning Free-Computation Alerting Streaming Analytics Analytics and Discovery Geographic Language Entity Relationship Event Translation Grab Dense/ Sparse Streaming indexes Views and Indexes Data Management Single, secure repository for all structured and unstructured data Data Lake Technology & Infrastructure The technology platform for storing and managing data Structured Unstructured Streaming Provisioning Deployment Monitoring Workflow Metadata Tagging Data Sources Infrastructure & Management Example 3: Below is a small sample of the types of questions organizations would be able ask of their DDOF analytic platform What impact does a 10-percent improvement in drilling efficiency have on my reservoir models? How much more surface capacity will I need and when? Given this increased efficiency should I employ less rigs and keep volumes the same? Where are my operational and technical bottle necks? Currently answering any one of these questions would be very time consuming; however, with a strong analytic platform they could be only a few clicks away. 7

10 Conclusion Data Driven Oil Fields can help organizations answer their most difficult questions and solve their most challenging problems. HSSE can be enhanced through preemptive maintenance strategies. Production optimization can be achieved through the integration of historical and real-time data. O&G companies can experience increased labor utilization through the use of centralized control centers, and powerful analytic platforms that can return results in minutes not days. Figure 9 Sample Benefits of DDOFs to Oil and Gas Companies Key Drivers Production Optimization Sample Benefits of DDOFs Improved Drilling Efficiency through integration and analysis of operational data Enhanced Understanding Of Production Volumes through advanced metering systems Unparalleled Resource Management through development of fully integrated forecast models Labor Utilization Reduction of Required FTEs through the use of fully integrated control centers Socialization of Institutional Knowledge of senior staff through knowledge management systems HSSE Enhancements Increased Visibility through advanced tracking and monitoring systems Reduced Equipment Failure LTIs through predictive maintenance systems Reduction of Human Error through automation of critical processes The implementation of Data Driven Oil Fields, requires three key components: security, integration and analytics. Together these components will help O&G companies improve existing processes as well develop entirely new workflows through unparalleled interconnectivity. About the Authors Atif Kureishy is a principal with Booz Allen Hamilton with over 15 years of experience in data science supporting clients in the US and abroad. He leads the regions Technology and Analytics Practice. His experience includes leading large teams of cloud data scientists, leveraging big data technologies against large-scale data sets for analysis and information discovery. He has delivered system development capabilities including: software architecture, development, prototyping, testing, training, operational site deployment, and life-cycle support. His expertise spans a broad range of industries including Intelligence, Defense, and Oil and Gas clients in the GCC. 8

11 Figures 10 and 11 Illustration of Improvements Within Existing Work Streams and Target State Connectivity Across Departments Exploration Extraction Transport/ Supply Chain Refining Export Data driven automation of seismic interpretation Simulations and scenario modeling Real-time monitoring of drilling mechanics to increase ROP Real-time monitoring of log data to improve geo-steering Automated work permit processes on rigs Automated people management program on rigs Real-time monitoring of wellhead and pipeline data to monitor flow rates Automated inventory logging system Automated supply chain operations for remote locations Real-time monitoring of rotating machinery and pressure systems Passive and activate security control and monitoring of plants Enhanced metering systems at point of discharge Well Surveillance Refining Drilling and Technology Contracts & Procurement Reservoir Management Strategic Planning HR Exploration International Marketing Assets Management Logistics and Supply Chain Major Projects Mustafa Dafalla is a senior consultant with Booz Allen Hamilton. Aligned with the energy practice, Mustafa brings a wealth of operational experience in the Oil and Gas industry. He has significant experience in real-time analytics for well delivery in both land and offshore environments. He also worked in the business environment helping a regional National Oil Company optimize their production through integrating their work streams. Mustafa has also worked in the region on the implementation of data-analytic platforms to tackle customers' most pressing issues. 9

12 International Presence Operating Office Principal Office Global Headquarters About Booz Allen Hamilton Booz Allen Hamilton is a leading provider of management and technology consulting and engineering services to governments in the civil, defense and security sectors, as well as to global corporations in all primary sectors. In the Middle East and North Africa (MENA) region, Booz Allen builds on six decades of consulting experience to partner with private and public sector clients to solve their most difficult challenges through a combination of business strategy, digital innovation, data analytics, cybersecurity and resilience, operations, supply chain, organization and culture, engineering and life-cycle project management expertise. Booz Allen operates out of six regional offices located in Abu Dhabi, Beirut, Cairo, Doha, Dubai and Riyadh. Headquartered in McLean, Virginia, the firm employs more than 22,000 people globally, and had revenues of $5.48 billion for the 12 months ending March 31, In 2014, Booz Allen celebrated its 100th anniversary. To learn more, visit boozallen.com. (NYSE: BAH). For more information contact Dr. Walid Fayad Executive Vice President MENA Energy fayad_walid@bah.com Dr. Adham Sleiman Vice President MENA Digital sleiman_adham@bah.com Atif Kureishy Principal MENA Technology & Analytics kureishy_atif@bah.com To learn more about the firm and to download digital versions of this article and other Booz Allen Hamilton publications, visit Booz Allen Hamilton Inc. C