The 'data crunch' how oil and gas executives could use Big Data as a powerful source of competitive advantage

Transcription

1 global energy specialists White Paper The 'data crunch' how oil and gas executives could use Big Data as a powerful source of competitive advantage Copyright 2013 Molten

2 Contents Executive Summary 3 Introduction 4 The Scale of the Issue and the Opportunity 5 The Missing Ingredient 6 Data Diversity 7 Value Model for Data 8 Getting Laser Focus on Data Management 11 Demonstrating the link between Data Management and Business Performance 13 Conclusions and Recommendations 15 Data Management White Paper

3 Executive Summary Key Take Aways 1. Data costs huge money. Yet, while careful about investing billions of dollars to acquire data every year, some oil and gas operators are borderline irresponsible about how they maintain it. 2. In addition to profit and market impact it s not enough for executives to consider people, process and technology when assessing their business initiatives. Now we must consider data too. Good Data Management drives business performance and mitigates risk. 3. People who manage data in Exploration and Production (E&P) businesses generally are given very limited career development possibilities whereas people who manage other assets are more highly regarded. Data management is generally a career cul-de-sac. This point is emblematic of the whole data management issue. 4. Get it wrong and it s wrong on a macro and a micro scale. On a macro scale the impact includes tragedies such as loss of life, environmental disasters and the potential to bring down the biggest companies in the world. At a local level wide-scale inefficiencies and significant lost opportunities result. 5. Get it right and significant efficiencies flow. Good Data Management drives business performance and mitigates risk. Short Summary Companies within the oil and gas sector face increasing pressure to make quick, effective decisions if they are to maintain production momentum and high levels of performance. As a consequence, they are becoming highly dependent on data, and more recently 'big data' to support these critical decisions. Paradoxically, recognising the value of this data and the need to manage it as a valuable asset is not so widely accepted at an executive level. Unchecked, the relationship is therefore one that could possibly lack reward and at worst prove unsustainable. However, this is beginning to change as a small but growing group of oil and gas executives begin to recognise the value of data and choose to harness it as a powerful asset to drive competitive advantage. For example, with the Final Investment Decisions (or FIDs as they are often known) that promote projects into production leaning on critical data and high level metrics, it is vital the information that underpins these decisions is well maintained; that it is robust and dependable. With technology driving data growth at an exponential rate, oil and gas companies need to be prepared to receive and manage this big data alongside considerable existing pools of information, if they are to harness its potential to fuel superior performance. 'Big data is data that is used to inform key business decisions. It is big because there is so much of it and is increasing in size and is therefore difficult to process, store and present. In global energy companies data intensity is high as is the need for proper data organisation and access. Molten s report, which examines the issues data management presents, shows that accuracy in data intensive measurements such as the reserve replacement ratio (RRR) has increased although at an average uncertainty rate of 10% we argue there is room and requirement for further improvement. The rewards in doing so appear to be high, with those companies exhibiting greater maturity in data management achieving the highest performance in reserves replacement. Put simply, this indicates that better data management drives better business performance. The scale of the data management issue is vast, with typical supermajors investing $1 billion $3 billion per annum on the acquisition of data a considerable sum even in a sector where big numbers are frequently bandied. Yet for all the dollars, investment in maintenance is scant, less than 1% of the acquisition cost, as executives do not consider it an asset as they would other means of production. This is not the executives fault per se; rather, the conditions in most companies do not exist for them to realise data s business value. The report proposes that data acquisition is costed and capitalised on the balance sheet in accordance with generally accepted accounting practice. This is the financial value of the data. In addition we propose a method for understanding the operational value of data and advocate that such a valuation is emphasised in management reports. This will ensure that appropriate internal importance is attached to data and its management. In valuing data for operational reasons initial costs should be combined with its fitness for purpose, the cost of its management and the potential for negative impacts in the event that the data is potentially bad. By creating an overall Data Value Framework and adopting a forensic approach to measurement, strengths and weaknesses can be determined and addressed. The business ownership of data, strong governance and a high performing data management organisation are all critical elements to successful data management within oil and gas companies. Molten advocates a single appointed senior business individual being ultimately accountable for the decisions regarding data and its management. Overall, we believe this paper can provide oil and gas companies with a greater understanding of data management, how to improve it and why its improvement is key to better commercial decisions and operational performance. Our research shows us that improvements have been made in the use of data but that if companies are to successfully respond to the increasing demands of the market place and the increased pace of decision making then for many there is more to be done. 3

4 Introduction The dependency of the upstream oil and gas industry on data and information to support critical decisions is universal. Recognising that data has value and the need to manage it as such is not so widely accepted. Through correlations between fundamental business performance of a range of oil and gas companies and their relative maturity in data management, this paper illustrates that managing data well is not only valuable but also essential to any oil and gas business serious about its own performance. Advances in digital technology are enabling oil and gas companies to collate data at an almost dizzying rate, however, if companies are not fundamentally equipped to manage this influx of big data the conclusions it might yield may be unfounded and the potential of the data unfulfilled. As a result the sector faces an immediate challenge. For those that can meet it, the commercial rewards will be significant. In reality, those responsible for data management in oil and gas operating companies often struggle to articulate the value of what they do to executive management. Yet, there is a means of valuing data and presenting a strong business case for managing it as an asset. This paper will argue for executive management s need to understand data s value in the decision making process and to sponsor a value-driven approach to managing it with clear business accountabilities assigned. Data Management White Paper

5 1. The Scale of the Issue and the Opportunity Despite the fact that data, and more recently big data, will have been discussed many times directly and indirectly during the normal course of conducting business, the word itself has connotations which make it difficult for business executives to understand its importance to meeting their objectives. The term conjures up mental images of bits and bytes' and hard drives causing at best a reaction of that s IT and nothing to do with me. Often the quickest route to securing attention is to use shock tactics. The consequences of bad data can include any of the following: Loss of life. Loss of a valuable asset or license to operate. Loss of credible reputation amongst the investment and sovereign government communities. Alternatively we can consider the critical data-intensive decisions that define the success of an oil and gas operator and consider the consequences of making these fundamental decisions poorly, which include: What is the next basin in which to invest exploration dollars? Where should we drill exploration wells? What type of reserves should we focus on unconventional, tight light oil, deepwater offshore? Which reserves should we commercialise? What type of production wells and facilities should we use? How do we optimise our production rates while optimising recovery factors? What is the best way to get the produced hydrocarbons to market? When will it be right for me to shut down an operation and start decommissioning - and how much will it cost? Even if oil and gas companies typically do not manage their data as an asset, it is true to say that typically every oil and gas company invests significantly in data. According to research recently undertaken by Molten, upstream divisions of supermajors spend in the range $1 billion $3 billion per annum on the acquisition of data (excluding management and technology costs). All of these acquisition costs should end up somewhere on the balance sheets of the companies in question. Rory Colfer, Managing Partner at Molten: Applying a straight-line depreciation over 10 years to this data equates it to an asset worth more than $10 billion for a company that invests $2billion annually in the acquisition of data" Even if oil and gas companies typically do not manage their data as an asset, it is true to say that typically every oil and gas company invests significantly in data. According to research recently undertaken by Molten, upstream divisions of supermajors spend in the range $1 billion $3 billion per annum on the acquisition of data. Rory Colfer said: Applying a straight-line depreciation over 10 years to this data equates it to an asset worth more than $10 billion for a company that invests $2billion annually in the acquisition of data. With $10billion an oil and gas company can acquire several production facilities. It s inconceivable that any physical assets of equivalent value would not have ongoing investment in their care and maintenance. However, typically this asset oriented mindset for data is rare in the oil and gas segment, because business executives, through no fault of their own, do not or, more accurately, cannot see it in that way. We need to enable them to learn and appreciate the potential value and then create the conditions to harness it. 5

6 2. The Missing Ingredient People, Process and Technology are the three main infrastructure enablers most business managers recognise as key to improving and sustaining business performance. Data is typically forgotten in this respect - whereas in reality it has at least the same level of influence on business success. Understanding the role data performs in supporting key business decisions and workflows is essential. Getting business leaders to understand that data is inextricably linked to business process is not easy. However, we can demonstrate the high level relationship in the 5 tier business model shown in Fig. 1, linking business strategy through organisation, process and technology to data. Strategy: sets the strategic objectives, values and policies for the company. Strategy People Process Technology /Tools Data Exploration Development Production Refining & Marketing Strategic objectives Strategic goals for critical business decisions to aim towards Standard organisation model Critical decisions taken to achieve strategic goals Segment-wide common processes Clear, consistent critical decision making processes Standard tools and applications Tools to simplify and accelerate process Proactive management of the data tier Accurate, complete, timely, accessible data to drive decisions based on the ground truths People: a well-designed and highly effective organisation that enables the business to make and execute decisions required to realise its strategic objectives. Process: standard decision making processes and workflows, to enable efficient decision making that is effective and consistent. Technology / Tools: help to automate, accelerate and ensure consistency in the core processes. Data: forms the factual basis upon which all interpretations, conclusions and decisions are made, whether strategic or operational decisions. Data is input to and created by all decision making processes. Fig 1. Five Tier Business Model showing linkage between business strategy and data * Big data is a collection of complex data sets so large that they are often difficult to process using traditional database management tools or data processing applications. Among the challenges in managing these data-sets are access, analysis, capture, curation or maintenance, definition, secure deletion, presentation or visualisation, search, sharing, security, storage and transfer. Data Management White Paper

7 3. Data Diversity The intrinsic nature of data changes as one moves from exploration through to production in the E&P value chain. At the E end the emphasis is firmly placed on relatively static and large volumes of petrotechnical data such as seismic and well logs which are expensive to acquire and whose half-life is relatively long. It is here that companies land their calculations on reserve replacement ratios announcements which sway opinion about their performance and which impact their share price. In the middle of the value chain, the major development projects design and construct expensive production platforms and facilities where significant amounts of static engineering, design data and documents are shared amongst several players including Engineering, Procurement and Commissioning (EPC) third parties and their subcontractors. Moving along towards the P the real time nature of data becomes more prevalent, such as flow rates, production rates, maintenance schedules and control of work, and the cost of acquisition decreases but its value diminishes rapidly over time. At the production operations end of the value chain, real-time data has more value, thus, the management of production operations data tends to be more operationally intensive and requires well-defined, repeatable, standard processes and clearly defined roles and accountabilities. The decisions made are less strategic and financially critical than the decisions made with longer half-life data, however the consequence of bad decisions using this data can be severe, including loss of life and loss of assets. We are now naturally moving into the realm of how businesses realise value from the entirety of their data asset and to understand that better we need to conceive of and apply a value model for data. Towards the P the real time nature of data becomes more prevalent. 7

8 4. Value Model for Data We have shown how the nature of data varies across the hydrocarbon value chain, however the method of putting a value on data is consistent throughout, and should be derived from the following four assessments for any given data type, as shown in Fig. 2 Business Value of Good Data 1. The business value of good data the value of all key business decisions which are dependent on fit for purpose data. 2. The cost of data acquisition and how it is represented on a balance sheet as an asset. 3. The cost of data management the activity required to ensure that data is available, fit for purpose and accessible. This includes stewardship (setting and ensuring compliance with appropriate standards), data maintenance, archiving and records retention. 4. The potential business impact of bad data on decision making in human, environmental and business terms e.g. loss of life; loss of a valuable asset or license to operate; loss of credible reputation amongst the investment and sovereign government communities; the list goes on. We will now consider each of these four factors in turn:- 4.1 Business Value of Good Data The business value of data to oil and gas companies relates to the role that data plays in making critical decisions well. The value of different types of data to a given company will vary depending on where the company in question is generating its value. For example Company A might excel at exploration and development in technically challenging frontier basins (e.g. pre-salt or arctic) whereas 'Company B' may find its competitiveness in the area of operations efficiency on mature onshore assets. The strategies and strengths of each are intrinsically different. The key business decisions for the former will be in the exploration and development activities (e.g. select basin, acquire data, target locations, acquire licence blocks, shoot seismic, drill exploration well, drill appraisal well, appraise, develop etc ) whereas for the latter it will be in the production area (optimise reliability and maintenance, execute turnarounds, optimise production, maximise recovery factor, reduce lifting costs, delay decommissioning etc ). Having identified the key value adding Fig 2. Cost to Acquire Data The Data Value Model Data Value Business Impact of Bad Data Cost to Manage Data activities for your company, the next step is then to examine the dependency on fit for purpose data in performing those activities and related decisions. A high level example of such an analysis is shown below for Company A in Fig. 3 below. This value chain diagram highlights Company A s highest value activities in green. Of those the ones further highlighted in red have a high level dependency on fit for purpose data. It is there that Company A should focus efforts on highly effective data management to drive business performance. Fig 2. The Data Value Model Data Management White Paper

9 Fig 3. High level representation of the oil and gas value chain, with highest value business processes and their dependency on fit-forpurpose data. Highest Value Creation Business Activities for Company A High dependency on fit for purpose data Strategy & Planning Enterprise Strategies Definition & Management Financial Performance Planning & Management Risk Management & Regulatory Compliance Portfolio Management Stakeholder Relationships Management Business & Competitive Intelligence Maintenance Knowledge Management A B2 Exploration & Appraisal B3 Development B4 Production B5 Refining, Marketing, Transportation B1 Operations Support Logistics Project Management HSE B Operations Basin Selection Opportunities Assessment Licence Acquisition Exploration Appraisal Engineering Support Concept Dev. & Basic Definition Well Development C Pipeline Development Production Unit Development Commissioning & Start-up Maintenance Management Production Operation Reservoir Management Well Mgmt. Decommissioning Operations Management Fuels Value Chain Deal Opportunities Identification Ship & Store Products Purchase & Sell Products Marketing Deals Administration Support Functions Treasury Information Technology Finance Procurement Human Resources 3rd Part Management Legal Compliance Physical Infrastructure 4.2 Cost of Data Acquisition Oil and gas companies spend a significant proportion of their cash on buying huge data sets (e.g. seismic), so much so that it could be argued we ve been dealing with big data for some time. One of the challenges in understanding the value of the resulting data asset is that the cost of acquisition is not fully understood across the whole organisation. A study undertaken by Molten showed that around 70% of total data holding is technical data (compared with financial or general business data) and the majority of that is in the subsurface and wells area. A high level analysis of annual spending on petrotechnical data acquisition by another supermajor showed the following annual spend profile:- Seismic - $1,000m Well Logs - $600m Reservoir - $250m Measurement / Logging While Drilling (MWD/LWD) - $100m Engineering - $1,080m Plant Equipment - $100m Often the value of these investments is entered on the balance sheet as assets and depreciated over time. Many times they are not, and are thus more difficult to identify when trying to get an overall true aggregate of spend on data. This requires a change in the approach to how such data acquisition spend is categorised and recorded at least for the purposes of management reporting, if not accounting. With these insights, the value of the asset can be properly understood and appreciated and the data can start to be treated like an asset and put to work to generate value. Until these insights are obtained and shared with executive management, we will continue to see that data is not managed like other assets in oil and gas companies. 9

10 4.3 Management costs In the studies and research that we have undertaken we often see that less than 1% of the value of the data asset is invested on an operating cost basis per year to maintain it. For any other type of asset in an oil and gas company this would be seen as inadequate and perhaps bordering on irresponsible. The due level of care really manifests itself in having an appropriate data management organisation, processes and tools in place. The cost largely translates to people and systems. In many cases the data management profession is not run as a profession and it is difficult to know exactly how many data managers there are actively executing data management processes and activities. However, a 2012 study undertaken for a supermajor revealed that there was in excess of 600 staff (both full time and contracted) actively engaged for at least some of their time. A subsequent study on whether this number was adequate and proportional to the value of the asset showed that it was significantly under what it should be. Interestingly there is not a direct correlation between the number of data managers required and the cost of acquisition for a given data type. In fact there is evidence to suggest that the opposite is true. In the case of seismic data, which is very expensive to acquire, there is often only one person accountable for its management for a particular region, and each of these people have other accountabilities in their job description. However, in the case of operations management, for example, where data is much cheaper to acquire but has a much shorter half-life, there can be several people required to manage the rapidly changing operational data landscape in a region e.g. adding data about new suppliers, delivery locations, new equipment specifications, new contracts etc. To fully understand the management costs and appropriate investment to make in managing data well it is important to understand the plethora of business processes that a given data type supports and the effort required to ensure it is fit for purpose. As stated previously, in general data types with shorter halflives will require commensurately more management than those with longer halflives as the relevance of data expires much more quickly. To determine the appropriate scale of resource, data management activity models can be created. This means modelling all of the data management processes, similar to the way business processes are modelled, and determining the amount of time and effort required to execute each step for each data record. By knowing the number of data records to be processed or managed during a given time frame we can simulate the amount of time and effort required to successfully manage that data and estimate the number of resources required. A recent study undertaken for a different supermajor showed that up to 10 full time people were required for a particular region just in the area of sustaining Materials and Services data for topside operations management. 4.4 Potential business impact of bad data The consequences of using inappropriate or incomplete data to drive key business decisions can be catastrophic. One measure of successful data management is a long and hopefully eternal record of avoiding such incidents. Unfortunately, such diligence is often not recognised or understood by executive management when things are going well. Only when the disaster happens, and root cause identified, does the understanding come. Of course it is not human nature to share insights into such incidents, but it is important for all of us to acknowledge in the oil and gas industry that in the real world such incidents do happen or are only narrowly avoided. A number of anecdotes and their financial consequences are described below to illustrate:- Exploration - An individual (now retired) was talking to the head of region as they were about to abandon exploration rights. He pointed out that data had been acquired 15 years previously. The report was found under a desk, after searching the office. The outcome was recognition of a target worthy of exploration which would have been given up due to the lack of good data management. Root cause poor management of exploration data. Subsurface - Field tapes containing critical seismic lines went missing in a particular region. The potential consequences included loss of operating licences and substantial fines. Root cause poor management of seismic data. Wells The geodetic references for two separate data sets on an onshore gas prospect were different. However, the two data sets were combined using the same geodetic reference. The resulting analysis concluded that the potential was poor. Some time later an intern came across the anomaly and reported it. The appraisal was rerun and showed high potential for prospecting. As a result an appraisal well was drilled, resulting ultimately in one of the highest flow rate gas wells in North America. Production - Poor/inconsistent workflows across disciplines led to 3 self-operated and 1 partner-operated fields using faulty fluids models in fiscal allocation calculations. For just one of these fields, the impact was $3m per year - for 6 years. Furthermore, analysis of the root cause of several well-known industry tragedies which have led to loss of life, significant damage to the environment and severe financial impact, shows some connection to inadequacies in data management amongst their root causes. Two notable examples are:- Macondo, the Gulf of Mexico Well blow out in April 2010, where failure and misreading of kick detection data by the onboard crew was a key factor in an incident which took the lives of 11 oil workers and spilled 4.9 million barrels of oil into the gulf. It has also led to tens of billions of dollars in expenses, fines and compensation payments in addition to the loss of the asset. The consequences seriously damaged all the involved businesses. Piper Alpha, the Occidental operated North Sea production platform explosion in July 1988, where shortcomings in the exchange of maintenance information between shifts resulted in oil and gas fires which killed 167 men, destroying the platform and leading to billions of dollars in financial impact. Data Management White Paper

11 5. Getting Laser Focus on Data Management 5.1 The quantum of data So far in this paper we have been referring to data types. We refer to each data type as a data object. Each data object is a defined collection of data which serves a discrete purpose in supporting business processes. Two examples:- Seismic data is a data object which helps drive the exploration business process. Purchase order is a data object which helps drive the asset operations business process. Each Data Object must be clearly defined for it to be managed properly as an asset, in the same way that physical assets are defined. 5.2 Governance - assigning accountability for data assets A critical success factor for managing data assets well is to have the right organisational structure and people in place and to clearly assign accountabilities for different data objects. There should be only one person who has ultimate accountability for making all decisions about a given Data Object for a company including the way in which that data object is managed to support all business processes which use it. Fig. 4 below shows how in many cases the same data object may be used by a number of different business processes which are owned by different parts of the organisation and served by different IT systems. For example, engineering equipment data are first defined by the engineers who are designing a plant, which is then handed to the major projects division to build the facility in compliance with the design, and eventually is passed on to the production operations teams to run and maintain the plant. So which part of the business should take responsibility for ensuring that data is fit for purpose for all those processes? Intuitively, in this example, we might say it is the engineering division who should take accountability for engineering equipment data. However, Molten s view is that it is the one which is exposed to the most risk if that data is served up with poor quality. In this case, it is the production operations teams who are presented with the most risk if this information is incomplete, Organisation Engineering Major Projects Production Operations Process Set Eng. Standards Design & Construct Operate Data Data Objects Systems Engineering Data Store Projects Data Store ERP Ops Fig 4. The same data object may be used by multiple business processes and organisational functions and handed off between different systems. 11

12 inaccurate or inaccessible. It is they who carry most exposure to the risks of loss of life, to significant impact on business reputation, or to non-compliance with legal or regulatory frameworks potentially resulting in loss of license to operate. If they have inaccurate information about the equipment on their plant, then the risk of not maintaining it properly becomes very real. The potential consequences of poorly maintained equipment on a production asset which will last up to 25 years do not bear thinking about. So, although counterintuitive on the face of it, it is arguable that the production operations function should take accountability for the quality standards of engineering equipment data, and involve engineering and major projects in its governance. The general principle to be applied is the business function with the most risk exposure to poor quality of a particularly data object should be given accountability for it, while ensuring involvement of all other divisions who also use it. 5.3 People We must not forget that it is people who manage any asset, and data is no different. In general, managers of physical assets are well respected and well managed, with plentiful career and personal development opportunities. Conversely, the profession looking after the data asset is not run as a profession and data managers are not recognised as would befit the value of the asset they care for. Furthermore they are given very limited career development opportunities. In short they are not set up for success. The ability of a company to provide a duty of care to managing data is largely reflected in the capability and effectiveness of the data management organisation. People involved in managing the data should feel valued, and feel that they have a fulfilling career ahead of them. Changing the mindset of business executives about the value of data will help create such conditions. The work that needs to be done to establish and sustain such a rewarding professional environment for data managers includes formalising the discipline. Roles must be appropriately defined and standardised in terms of required skills, competencies and experience with appropriate management grades afforded to reflect relative seniority and decision rights. Who these roles report to is also important. Data is a business asset and should be ultimately owned and managed from within the business. However the activities to manage the data asset must be shared between the business and the IT department. The technical aspects of providing digital storage, access tools and interpretation schools will be managed from within the IT department, but it should be noted that IT has a T in it and it is managing the technology that is done there not managing the data. By optimising the technology that underpins data management (software, hardware, network capabilities) significant benefits can be delivered, including in Molten s experience, improved efficiencies equating to a 20% reduction of time spent on searching and analysing data. Career development opportunities should span both business and technology aspects of data management, so people managing the data know not only how to manage the data technology well but also have the important context of how the business is using the data to make critical decisions that affect the company s performance. Data Management White Paper

13 6. Demonstrating the link between Data Management and Business Performance 6.1 Data Drives Business Performance Put simply, better data management leads to better business performance, the benefits of which will include fundamentals such as:- Revenue increase improved finding efficiency of up to 3% and better operational results from better informed decision makers. In an environment where final investment decisions (FIDs) the decisions that bring project plans into production will need to be made faster in order to create momentum in production, being able to rely on sound data will be crucial to success. Where the data is complete and well maintained, quicker decisions can be more confidently and efficiently made by management teams. Risk reductions sustaining licences to operate, maintaining the value of data assets, avoiding unintended data loss, disclosure or damage to reputation. Accessible benefits are linked to the change and cost of catastrophic loss through operating with poor data. Mitigation of the most catastrophic risks can translate to avoidance of billion dollar liabilities, not to mention loss of life. Katerina Brazhnikova, a Partner at Molten and Head of their Russian and CIS region, said: We are excited about some of the very real and tangible business performance benefits that are possible through enhanced data management. For example, we managed to increase the efficiency of reservoir engineers and production managers of one of our clients by 15% by simplifying and standardising the reservoir modelling data management processes. This helped to eradicate low quality data and thus increased confidence in underlying data leading to the outcome that we all wanted - significantly improved critical decision making efficiency. Acquisition cost reduction reducing the cost of data acquisition, e.g. by preventing duplicates of expensive data sources such as seismic, can yield tens of millions of dollars per year based on 5% optimisation Opex optimisation reducing the cost of data management by at least 2% through standardising processes, technologies and formalising the organisational capabilities. 13

14 Data Management - Competitor Comparison Undisciplined Reactive Proactive Governed 6.2 Deep Dive into the Reserve Replacement Ratio Issue To illustrate data s influence on a major business performance indicator, we will examine its relationship with the Reserve Replacement Ratio (RRR), a key, high level measure that determines a firm s ability to sustainably produce oil and to grow. Data management is a key component in the forecasting upon which executive teams base decisions and on which financial markets assess companies. A number measurement of 100% indicates that a company has replaced all the reserves it has extracted, while a number below 100% raises questions over long term sustainability. Our sample excludes National Oil Companies (NOCs) Data Management Maturity Source: Molten research Supermajors Large Independents Other Independents Fig 5. Comparative data management maturity curve across oil and gas segments. Rory Colfer: A 10% error rate is deemed acceptable in the sector. However, I think we need to question if this is level of error is appropriate. Demands are becoming more acute. Management teams are under pressure to keep momentum behind production, yet as an industry we re saying it is OK to base decisions on measures such as these that can be prone to significant error. Clearly the nature of exploration and production means there will always be an element of error. However, I think we can do better and over time reduce the error rate across the board. The key to producing better forecasts is better data and its management. Molten research shows that the level of accuracy firms achieve in assessing their RRR varies widely. Some achieve low error rates of 4% and others are further adrift at up to 14%. Unsurprisingly, the average error rate for the sector is 10% - in other words expect accuracy of RRR to be on average 10% above or below what transpires to be the real level of results. This is exactly in line with the Securities and Exchange Commission (SEC) definition of P1 proven reserves, and the inbuilt uncertainty of 10% that is permitted. However the key question here is should the industry be content with that, especially given the trend over time is that the accuracy of RRRs appear be increasing? Certainly, those companies consistently adrift of 10% have the opportunity to increase confidence of the investment community through better performance in this respect. Molten s research also applied anecdotal evidence gathered through its own work with high level business performance data. The business performance metrics were obtained from publically available data and included reserves replacement and lifting costs. The data management maturity assessment was based on Molten s own insights gained from working with different companies across the industry, its involvement in industry data management conferences and hence access to oil and gas companies presentations giving insights into their data management maturity, application of its own data management maturity model to those insights in addition to conclusions from a high level study undertaken on behalf of a client. Data Management White Paper

15 We found three interesting facts:- 1. Different oil and gas companies are at different stages of maturity in relation to data management and the key point - the general trend is towards an increase in maturity (see Fig 5). 2. There is no particular segment within the oil and gas industry that is excelling at data management compared to other sectors i.e. IOCs, NOCs and Independents all have similar spreads in their performance. However, as a group, the larger companies tend to have a slight edge and the large and mid-tier independents as a group are perhaps between one and three years behind their supermajor brethren in their data management maturity. 3. Among IOCs, where reserves replacement data is more widely available, those companies with greater maturity in data management are the highest performing in reserves replacement an interesting indicator that better data management drives better business performance (see Fig 6). Reserves Growth vs. Data Management Capability Proactive Reactive -5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% Our sample excludes National Oil Companies (NOCs) 5 4 Undisciplined Year CAGR of Reserves ( ) Vs. DM Capability Supermajors Large Independents Other Independents Fig 6. Comparison between reserves growth and data management maturity Source: Molten research 7. Conclusions and Recommendations Conclusions 1. Data is a very valuable asset. E&P businesses know this and pay vast amounts of money for it. Then when they get it they don t maintain it well. 2. Great data management drives good business performance. 3. Businesses do not have a structured approach to data management. This is evidenced by the variety of approaches to it even within one organisation. DM can also be a career cul-de-sac. 4. Get it wrong and major negative events can ensue. Recommendations Molten advocates a number of measures that it believes can enhance the performance of individual companies and the sector as a whole. These are as follows: Create a data environment in which the full potential of big data can be harnessed. For many companies this will mean formalising data governance and adopting better data management processes and organisational capabilities in situ ahead of scaling up for the increased quantity of data. The commercial benefits to doing this could be significant. Establish a uniform method of data valuation. By better understanding the value to a company this important enabler will be better maintained and as such will retain greater value. Establishing data as a balance sheet item will help executive teams better understand and value the asset within their business and a uniform approach will allow external stakeholders to assess it. Implement the concept of data "as an asset" and the consequences will be a valuable asset that is carefully managed. Enhance the accuracy and standing of the RRR, so that it can improve internal decision making and external evaluation. This can be achieved by adopting a more robust approach to data management. Improvements are certainly being made in accuracy and we believe that the sector can reduce the error rate to 7-8% within the next five years. 15

16 About us Moscow London Houston Molten is a specialist global management consultancy focused on the oil and gas segment. We have offices in three main global energy hubs in London, Moscow and Houston. We develop solutions for oil and gas companies in three main challenge areas: Technology, Innovation, Research & Development Organisational Performance Data and Knowledge Management Within these challenge areas we deliver all phases of transformation: Analysis and operational strategy development Operating model design Implementation and management of change For more details on how Molten a global transformational consultancy is achieving operational efficiency and organisational performance for Energy companies worldwide, call us on , go to or contact one of our experts directly: United Kingdom Russia United States of America Molten UK & Middle East 22 Grafton Street, Mayfair London W1S 4EX Molten Russia & CIS Maly Kislovsky Pereulok, 9, bld. 1 Moscow Molten Americas 800 Town and Country Boulevard Suite 300, Houston TX