Bringing Oilfield Data into the Enterprise

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1 Bringing Oilfield Data into the Enterprise Author: Shannon Tassin, Director Abstract: Oilfield of the future data reporting applications are built to enable faster decision-making based on better data. This has usually meant solutions that are specific to the hardware and software components that are installed in the field. Oilfield of the future initiatives result in an explosion of new data coming into the enterprise and create huge volumes of data that is difficult to manipulate and interpret. Many implementations have not integrated well with corporate data repositories nor have modeled data in a way that allows for easy integration into broader decision-making use. It is not until that data is integrated with maintenance schedules, equipment data, production information, and financial data that real actionable information emerges. This paper will describe new data integration, data quality, and master data management practices that should be applied when creating business intelligence applications for E&P operations. The Problem Consider a typical oil and gas company scenario: A geoscientist that works within a supermajor s Gulf of Mexico Production division is working to determine how to best increase the production from a mature field. He believes that by drilling wells in some areas of the reservoir that are not being drained effectively by the existing producing wells, he can increase the ultimate recovery factor from the reservoir. He has the horizon interpretation data from the Gulf of Mexico Exploration division that relates to the prospects that were originally drilled, but he cannot locate interpretations made in the parts of the reservoir he is now exploring and fears that they were never archived and passed along. He is not sure where to go or who to go to in Exploration to ask for the data and since it has been a few years since that work was done, and he is not sure any of the original team is still there.

2 This geoscientist has new pressure data showing how the reservoir has been partially drained, and suspects that other good, actionable exploration data exists. He just does not know how or where to find it. He envisions a solution that would link together all technical information in the enterprise so that known, trusted, and integrated data from across the E&P lifecycle could aid in confirming his hunch that there is more production to be obtained from the field he is tasked with developing. Like many in his position, instead of facing what seems an extremely painful task of looking for information that may not exist and is of unknown quality, he chooses to start from original seismic and original exploration well data and re-interpret the data himself. At least that way, he will have confidence in the result! Upstream Data Management Challenges Core to this problem is the fact that oil and gas companies tend to be organized into functional silos. Prospect Explore Develop Produce Dispose Manage Basin Stratigraphic Structural Geochemical Interval Mapping Plan Formation Parameters Analog Flow Rate Porosity Delineate Well Developmt Assess Pressure Controls Design Production Reservoir Analyze Prod Data Well Testing Abandonment Eval Deeper Targets Shallow Hazard Regulatory Land Management Contracts & Agreements Leases & Concessions Play Developmnt Seismic Spatial Impedance 3D Survey Planning Drill Core Points Mud Logs Hydrocarbon Identification Zone Target Exploitation Simulations Reservoir PVT Drill Stem Testing Workover Completion Assess Recovery Techniques Reclamation Environmental Engineering Waste Management Financial Mgt Financial Acctg Economic JV Accounting Generate Prospect Volumetrics Economic Depth Migration Reservoir Evaluate Wireline Logs Productivity Index Frac Design Artificial Lift Complete Fluid Dynamics Perforations Casing Tubing Design Enhanced Recovry Reservoir Economic Disposition Determine Disposition Value Production Mgt Prod Allocations Partner Balancing DOI, WI, RI Figure 1 shows some typical functional divisions and the responsibilities that fall within each. Information inside of interpretation applications and data repositories are usually managed by groups within each functional silo so has tended to become specific to those silos. This approach to data management means that cross-discipline reporting, data analytics and business intelligence are rarely done the data is simply not typically in a position or format to support it. Figure 1 Noah Consulting, LLC copyright reserved page 2

3 Oilfield Data Acquisition Architectures Adding to the inherent challenges caused by siloed operations and information management models, most companies are expanding their implementation of oilfield of the future technologies. Those technologies bring new types of data, new amounts of data and new technical architectures to the table. Figure 2 shows a typical data architecture that can handle real-time data collected by current generation oilfield technologies. Data acquisition architectures typically deliver data either in real-time or in batch to historians, control applications, monitoring applications, and E&P technical applications. In some advanced cases, there may be an enterprise data historian that serves as a central collection point. However, if it exists, that central historian is rarely connected into the organization s enterprise data management architecture and is not usually considered a system of record for data. Figure 2 Noah Consulting, LLC copyright reserved page 3

4 E&P Information Delivery Architecture To solve our geoscientist problem and a host of others, E&P companies are increasingly realizing the need for and value of a consolidated information delivery capability. Technical professionals require the ability to find both structured and unstructured information regardless of where it was created in the value chain. They want to go to one place and have the technology handle searching for and returning all available data in contextual relevance to their business requirements and to the fragments of data they may have already acquired. When they find something that might meet their needs, they want to know where it came from, who created it, and whether it is the most up-to-date representation of the data. Beyond the needs of our single geoscientist, the demand for analytics and business intelligence within E&P is on the rise. For example, items like budget forecasts and reserves are based on production estimates, causing current and projected production variances to have a ripple effect throughout the company. Companies do not just want to know what the variance between the production estimates and actual production for the wells in one area; rather executives want to know that information company-wide. Requests like this are driving the demand for enterprise E&P business intelligence, which requires a comprehensive E&P information delivery architecture, as shown in Figure 3. Figure 3 Noah Consulting, LLC copyright reserved page 4

5 High-value information delivery capabilities like enterprise search and variance reporting are only possible if there are strong information architecture and standard data management processes in place. There are different potential methods to deliver such capabilities. It is critical that a holistic vision and strategy for information management is developed up-front and used as a roadmap for future projects and developments. Key Architecture Components & Disciplines While all of the architecture components (Table 1 below) and disciplines (Table 2 below) are important, there are a few that are especially important within oil and gas companies. The oil and gas industry is unique in its need to jointly manage and retrieve structured and unstructured data. Component Purpose Processes Technology Systems of Record Document Management Serve as the trusted source for a set of data types Manage unstructured information content Puts the proper controls to insure the system can be trusted Must be able to effectively function as the vault & handle retention policies Serves data to architecture components through standard interfaces Leverages master data & metadata to integrate with unstructured data content Data Management Manage structured information content Business rules overcome semantic disconnects to provide integrated data Leverages master data & metadata to integrate with structured data content Table 1 Noah Consulting, LLC copyright reserved page 5

6 A strong master data management capability ensuring that these different systems are all working from the same reference data elements (e.g., well number) is critical. Many oil and gas companies are implementing master data management systems using standard industry data models to both manage reference data that is being mastered, and also to store the critical metadata that is needed to allow the data to be fully utilized. Discipline Purpose People Processes Information Governance/Stewardship Information Quality Management (IQM) Master Data Management (MDM) Metadata Management Enforce roles and responsibilities across the entire information management lifecycle Produce accurate, trusted information to drive informed business decisions Define, mandate and manage a consistent definition of key business objects/entities Information about information, the glue that binds, providing context and lineage Sponsors, Data Stewards, Governance Committee, Subject Matter Experts (SMEs) Corporate Data Steward, Line-of- Business Data Stewards, Consumers, Producers, SMEs Business Analysts, Data Analysts, Data Modelers, IT/IM Professionals, SMEs Business Analysts, IT/IM Professionals, SMEs Clearly defined data management processes tied to business processes Consistent processes with repeatable and measurable results to drive quality improvements Creating, updating and archiving master data; integrating with applications and databases Definition management, management for business rules, schema, versions, lineage & history, exception handling, etc. Table 2 Noah Consulting, LLC copyright reserved page 6

7 Master Data Management Solution Options & Steps to Success Master data management solutions can be implemented in a variety of ways. Most importantly, they should fit easily with the broader information architecture and conform to its standards, guidelines and concepts. There are various descriptions of MDM styles in the literature some of which are appropriate in E&P and some being more suited for manufacturing or banking. The style of master data management (e.g., a centralized versus federated approach) that will work best will depend mainly on the organization of the company, the existing technical components and solutions, and geographical spread. Figure 4 shows options at each end of the spectrum and some of the pros and cons related to those options. Keep in mind that there are variances that have characteristics of both options that might best meet the needs of your organization. Figure 5 outlines a proven three-phase process to help companies choose and successfully implement their master data management solutions. Centralized MDM Centralized data entry with propagation to applications Pros Leverage of MDM tools for accelerated implementation Cons Disruptive to the business (reverses SoR and business process flow) Creates complex business dependencies (business functions have to wait on each other for data) Complex application changes needed to Systems of Record Many instances of technical success but overall project failure Federated MDM Authored in current Systems of Record and harmonized throughout the enterprise Pros Not disruptive to the business thus improving business adoption No changes required to Systems of Record Demonstrated recent success within the industry Cons Multiple tools need to be leveraged MDM solution itself is more technically complex Figure 4 Noah Consulting, LLC copyright reserved page 7

8 Figure 5 Critical Success Factors Bringing oilfield data into the enterprise requires more than just new oilfield technology. It requires an architecture that supports E&P enterprise data management strategies. It also requires the right process, governance and data quality capabilities to be deployed throughout the organization. New methodologies for implementing information quality, master data management and metadata are having success in oil and gas companies. The technologies and implementation methodologies are tested. However, like most large and far-reaching projects, it is the softer things that usually dictate whether success will be achieved. A focused approach, disciplined project management, a dedicated team and a solid architectural foundation are a few of the important success factors to keep in mind when implementing a system to bring disparate data sets and elements together. Noah Consulting, LLC copyright reserved page 8

9 Breaking down the historical silos and managing data in an enterprise manner requires a significant amount of behavior and organization change. Change is not easy and most people will default to their normal way of doing things without the proper motivations for changing. Business alignment, executive support and having the right business evangelist are critical when considerable cultural change is expected. With the right components and disciplines in place, oilfield data from field of the future initiatives can be integrated with other enterprise data like cost, HSE, and reserves to create highly valuable business intelligence capabilities and turn your siloed data into actionable information. Noah Consulting, LLC copyright reserved page 9

10 About the author: Shannon Tassin has spent more than 15 years developing technology solutions for clients, with most of that spent working with oil and gas clients. Over his career, he has developed expertise in data warehousing, data management, geographic information systems, upstream field of the future, and upstream technical applications. Shannon has delivered consulting services and led initiatives across oil and gas super majors and independents and is taking a lead role in managing Noah s delivery of consulting services to upstream energy clients. About Noah Consulting: Specializing in helping companies leverage the value of information, Noah Consulting focuses exclusively on delivering information management solutions to clients by providing best-in-class data management, data warehousing, data integration, business intelligence, information quality, and master data management services. Using a client-solution focused, product agnostic approach, Noah Consulting provides business solutions across the full lifecycle of services: information strategy, information architecture, and solution delivery. Noah Consulting is comprised of veteran consultants with a track record of uncompromised principles, proven success, and client focus. With an average of 15 years of experience in solving the most complex business problems, Noah's consultants and the solutions they implement are unrivaled in the industry. Noah Consulting, LLC copyright reserved page 10

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