Unconventional Upstream Data Model Turning Data into Action into Knowledge

Transcription

1 White Paper Unconventional Upstream Data Model Turning Data into Action into Knowledge Executive Summary Producers of Coal Seam Gas (CSG) or Coal Bed Methane (CBM) outside Australia face significant pressure to improve operations and increase production results. Challenged to achieve in the order of five times the productivity of assets and people per well when compared to conventional oil and gas exploration and production activities, CSG operations must nevertheless maintain safety, reliability and efficiency to be recognized as a credible Upstream CSG Operator all this while managing local production sites from remote, centralized operation centers to best attract and retain key people. Other factors add to the challenge when compared to conventional wells: resource constraints; variable supply and market conditions; a mix of manned and unmanned facilities; extended, low margin well life-cycles; and of relevance, large well counts and associated supply chain assets. To help overcome this asset-intensive operation and the associated challenges, leading CSG producers are turning to innovative technology to uplift the effectiveness and productivity of their resources. Also, with the reality of Big Data driving the patterns of business processes, the effective management and recognition of what data is important, becomes all the more critical. As such, data is now recognized as a key asset for CSG operators, and architecting the right blueprints to best deliver services to support data management means data access, data modeling, and collaborative tools to support considered decisions and actions from the resulting information, is of fundamental importance to support these goals. Advanced software is now available to help transform process data into meaningful, actionable information. Mountains of data, previously not accessible, integrated or interpreted, can now provide integrated views of operations, production and supply chain activities so operations teams have a clearer view of all information, from strategic planning to accurate production measures. This paper describes how a standards-based approach and advanced software technology comes together to turn large volumes of exploration and production data into knowledge for better operational results.

2 Unconventional Upstream Data Model - Turning Data into Knowledge 2 Table of Contents 1 CSG Operations Drivers Exploration or Resource Plays Information Transformation based CSG Surveillance Projects Integrated Operations Excellence Industry Standards Analysis & Integrated Operations Collaboration Collaboration Background Drivers Collaboration Business Drivers The Role of Standards Scope Typical Function Scope Typical Process Scope Project Scope Outcomes Proposed Industry Standards-based Executive Semantic Model Workshop Model Example for Interoperability Conclusions & Recommendations Step 1: Think in Standards Step 2: Talk in Standards Table of Figures Figure 1: Conventional delivery processes An exploration play... 4 Figure 2: CBM A resource or exploration play... 4 Figure 3: CSG Supply Chain The Standards and Information Gap... 6 Figure 4: CSG Supply Chain The Standards and Action Gap Closed... 7 Figure 5: CSG Collaboration Addressing Unsustainable Operations... 8 Figure 6: CSG Collaboration Example Business Value... 8 Figure 7: CSG Model Scope of Functions... 9 Figure 8: CSG Model Scope of Processes Figure 9: Proposed Industry Standards Ontology for Production Model Industry Standards Figure 10: ISO15926 Data Lifecycle for CSG Production Models

3 Unconventional Upstream Data Model - Turning Data into Knowledge 3 1 CSG Operations Drivers CSG production is a low margin business relying on scale for a sufficient return on investment. Sustaining stable well operation and workflows is a challenge with 250 wells, yet CSG operators may manage as many as 10,000. Figure 1 on page 4 shows the unconventional exploration to market cycle. Several enhancements to traditional solutions are called for: More dynamic models to respond to upstream and supply changes to feed capacity models. This enables quicker reactions to downstream disturbances or irregularities in short term or reservoir operating plans. This ensures production accords with the production plan, that resource and maintenance plans can be aligned, and that the plan remains aligned with market demand. Process optimization through offline and event-based network simulations that model varying production and market conditions Improved resource management through remote collaboration and operation centers Workflows that react quickly and precisely to changing reservoir or process dynamics, improving identification of problems and addressing them in the planning phases or within sufficient time periods to respond. It might mean, for example, losing a few wells from the scope of the project in order to develop a more accurate route cause analysis (RCA) process back in the Petroleum Engineering Centre of Excellence. That learning can be used within an improvement project to improve operation of wells. This is likely to be better than redirecting and deploying significant numbers of high cost resources to site reacting to problems best dealt with by business process automation, dynamic reservoir modeling, or process simulation. Improved stochastic, rather than independent, well observation and control. Well numbers are likely to make it impractical for a technician to investigate an individual instrument failure, for example. Instead, operators will potentially opt for a bulk replacement when a number of such instruments fail. Similarly, changes in well settings at a stochastic level can be made to accommodate changes in demand. Wells should be grouped in clusters based on dynamic dependencies due to the shared systems of reservoirs and aquifers. This will also result in better advanced process control. The target is business transformation through the application of template solutions applied to wells and other facility assets to boost workforce productivity.

4 Unconventional Upstream Data Model - Turning Data into Knowledge 4 2 Exploration or Resource Plays Conventional delivery processes and techniques are considered Exploration Plays, given the focus and criticality on this activity in determining the viability of the commercial venture (refer to Figure 1 below). Figure 1: Conventional delivery processes An exploration play CBM, by contrast is best considered a Resource or Exploitation Play. A continuous cycle of appraisal and exploitation of the resource to quantify the potentiality is central to establishing the commercial case for a CBM or CSG venture. Figure 2: CBM A resource or exploration play

5 Unconventional Upstream Data Model - Turning Data into Knowledge 5 3 Information Transformation Transforming process data into meaningful, actionable information is one obvious way to support this drive. With genuinely integrated management of supply chain data for the Upstream CSG operation, operators have a clear view of all information, from strategic planning to accurate production measures. This allows us to close the Action Gap : moving beyond mere data collection and analysis to efficient orchestration of resources through automated tasks, workflows and business processes. At the same time, by making the information available to more end-users through collaboration tools, the organisation will see better knowledge transfer, improved KPIs, ASM notifications, analysis, and workflows based CSG Surveillance Projects To achieve these objectives, Honeywell s framework and associated based software solutions have been developed. They combine current and historical data from disparate sources, display it in a clear and simple context, and promote collaboration and automated responses. From mid 2010 to the end of 2011, Matrikon, now a part of the Advanced Solutions software group in Honeywell Process Solutions, worked with Two Tier 1 Australian Upstream CSG-LNG operations to determine the most suitable framework to support upstream CSG operations. Four solutions were used: Well Performance Monitor Alarm Manager Matrikon Production Manager: Downtime Reporter & Production Accounting Executive (IX) Spanning a range of services from measurement, analysis and data modelling to presentation services and workflow automation, the solution is predicated on the presentation of contextualised information managed via an abstraction layer. This abstraction layer, s Semantic Model, is built according to W3C semantic web standards. It is organised as Ontology, to maintain a virtual data repository describing the CSG infrastructure, facts, and knowledge. The abstraction layer dynamically manages and maintains connectivity to master data sources that collectively participate in federated data architecture. accesses the data it needs ondemand from these sources rather than copying and reconstructing it into another data store. It then presents it in context for users. This is Honeywell s Unconventional Upstream Data Model. Conformity to W3C open standards ensures more contextual data, from multiple complex and unrelated data sources supporting differing data types, is available to business user (such as OPC, SQL or Oracle, Excel, GIS, and Historian-sourced data). Ultimately it means more efficient data management than realised by a traditional relational data management system and an effective bridge over the data divide that currently exists between corporate offices and the site. Finally, Executive s technology services facilitate rapid scaling and less governance and maintenance compared to traditional point-to-point integration solutions typically supported by an aggregated relational database. This makes IX it well suited to the resource constraints and rapid operational changes in CSG production. 3.2 Integrated Operations Excellence Improved Operations Monitoring & Surveillance: Studies of one of the Queensland, Australia-based CSG Operations Monitoring projects looking at the effect on the workforce, systems infrastructure, and overall business position show the value of closing the Action Gap. A number of benefits were realised during the project: Promotion of an integrated data flow. Data became more accessible and contextual; automated data acquisition improved by 71% for 300 wells with 45,000 tags; by enabling operators to focus on post-processing & analysis, downtime events were minimised; the trend toward acknowledging data as a key asset and assignment of the role of a data owner was consequently promoted.

6 Unconventional Upstream Data Model - Turning Data into Knowledge 6 Enhanced analysis capabilities, such as enabling mobility to improve workflows relating to Water Pond sample readings, leading to greater flexibility for the production environment. Spreadsheet information islands were also reduced 350 that were maintaining intellectual property were identified and integrated. Overall collaboration and analysis improved by 67%. Streamlined workflows. Four primary business processes and 25 sub-processes were amalgamated into a three-step workflow, and the process cycle for well and plant data and rate allocations improved by more than 60%. Promotion of event streaming and Complex Event Processing technologies as a method to improve data validation, pre and post processing data analysis, task assignment and Workflows. Overall efficiency improvements reduced weekly management workflow from 56 days to just 18 days. 4 Industry Standards Analysis & Integrated Operations Industry Standards Analysis Shop-floor-to-top-floor is the mantra of the IT industry, but many barriers exist. In an effort to transmit information from measurement sources to the corporate enterprise resource planning (ERP) systems, or driving corporate plans down to those who action them, integrators are inhibited by incompatible standards for data structure, data transportation, vocabulary and services. This is the Standards Gap (refer to Figure 3 below). The result is Frankenstein s monster a disparate assembly of technology, applications and services; ultimately incoherent, unreliable, and untimely data. Figure 3: CSG Supply Chain The Standards and Information Gap

7 Unconventional Upstream Data Model - Turning Data into Knowledge 7 A standards-based approach and enhanced solution interoperability through (which maintains open standards down into its DNA), brings shop-floor-to-top-floor and the seam-to-sea come closer to reality. Combined with closing the Action Gap, the result is a vastly more efficient solution in which much of the process is able to be automated (Figure 4) KPIs Non Conformance Target Setting Figure 4: CSG Supply Chain The Standards and Action Gap Closed 4.1 Collaboration Enabling this to happen, however, also require better remote collaboration between Operations and Maintenance, and Physical Asset Control and ERP to efficiently control production and operational processes Collaboration Background Drivers The gas industry has seen unprecedented growth in operations and demand over the last 50 years while facing a squeeze on skilled resources. Over the next decade western oil and gas companies may lose 75% of their intellectual capital. Replacing this will require recruitment of a new generation of workers from a demographic that is used to having information delivered to their desktops and working collaboratively through multimedia. Companies need to engage this generation by providing appropriate environments and familiar tools. In any case, the limitations of silos are ever more apparent. Geophysics, drilling, reservoir, production, engineering, planning, finance and maintenance have each traditionally worked to different constraints and time lines. Reservoir engineers time horizons are months and years, while production technologists work to minutes or weeks. Yet short-term decisions taken by production can adversely affect draw down from the reservoir over its lifetime, and reservoir engineers plans may be wholly unrealistic in the face of weekly production or market constraints. Isolated decisions lead to missed opportunities. A final consideration is that operators must also accommodate workers desires to avoid living in undesirable locations, while at the same time, exploration looks to increasingly challenging and remote locations. Collaborative work environments can help address these challenges. Refer to Figure 5 on the following page.

8 Unconventional Upstream Data Model - Turning Data into Knowledge 8 Figure 5: CSG Collaboration Addressing Unsustainable Operations Collaboration Business Drivers There are also immediate business drivers for collaboration. A Cambridge Energy Research Associates (CERA) study 1 found significant benefits from the adoption of the intelligent oilfield, whereby the measurement of data and automation of the process is maximised and made visible in collaborative environments typically organisationally collocated in a single building or floor of a city tower block, in what is termed a Collaboration Centre. They included lower operational costs, earlier and increased production, lower capital investment, increased recovery of oil and gas, and lower abandonment costs. With significant scaling and asset and production management improvement, CSG operations should be able to achieve similar savings. The CERA study also states that field operator productivity can increase %, operating costs can decline 10-20% and average production rates can increase by 1-3%. In fact, case studies suggest these estimates are conservative. Based on results from an advanced collaboration centre and application of their digital oil field programme in the Norwegian North Sea Sector, BP 2 estimated it will be able to recover an extra 1 billion barrels of oil globally, representing 5% of current proved reserves. Likewise, Shell s review of Smart Fields 3 show benefits already realised of 8% in ultimate recovery and 10% increased production. This is the reality of business transformation. Figure 6: CSG Collaboration Example Business Value 1 Society of Petroleum Engineers [SPE] Paper BP estimate $5m per annum per asset through improved drilling operations: reference SPE Shell 8% Ultimate Recovery 10% Increased Production: reference SPE

9 Unconventional Upstream Data Model - Turning Data into Knowledge 9 5 The Role of Standards Having established the benefits of collaboration and of closing the standards and action gaps, Honeywell turned to establishing design recommendations that would allow the data exchange and interoperability of applications within the Unconventional Upstream Data Model. The first step is to standardise the data structure. The Semantic Model uses the RDF from the W3C standards group. This enables us to phrase statements in a simple atomic format such as well101 hasstate shut-in that can be applied to represent any data, whether from an ERP system, maintenance system or operations historian. The next step is to create a vocabulary. The hasstate predicate above, for example, implies a well can have a state. In this case, the well is not producing or shut-in. It could also be applied to well102, well103 and so on. We might also create a predicate called hasproductionrate to tell us the current production rate of the well. This is typical of most data modelling exercises, and in the absence of any standards, the naive approach would be to create a proprietary vocabulary that describes wells, compressors, gas plants, and other domain assets. However, trouble then arises when sharing information with other systems. It is therefore better to use a wealth of existing standards to define our vocabulary. During 2011, the CSG operators and Honeywell s Advanced Solutions business therefore consulted with key standards experts to look at leveraging existing industry standards for this purpose. 5.1 Scope Typical Function Scope The following diagram highlights the typical functions being observed. Some functions are being supported directly by the final solution, and others are being maintained in part. Further functional decomposition is required against requirements to ensure the data being modelled, and the views and analysis rendered, are aligned to the functions in scope. Figure 7: CSG Model Scope of Functions

10 Unconventional Upstream Data Model - Turning Data into Knowledge Typical Process Scope The following diagram highlights the typical processes being supported by the data model. Further process decomposition is required against requirements to ensure the data being modelled, and the views and analysis rendered, are aligned to the processes in scope. However, what is represented here is typical of high level processes maintained by most E&P areas, where the following acronyms are typically used to identify facilities: GCF is Gas Compression Facility CPF is Central Processing Facility WTP is Water Treatment Plant Manage Production Operations Manage Reservoir Plan Production Manage GCF or CPF or WTP Operations Perform Reservoir Analysis Determine Long Term Planning Execute Production Perform Reservoir Simulations Determine Short Term Planning Perform Plant Performance Analysis Determine Reservoir Operating Plan Manage Resources Figure 8: CSG Model Scope of Processes

11 Unconventional Upstream Data Model - Turning Data into Knowledge Project Scope The context of this paper relates to actual implementations. The project sought to establish the most promising standards for use within the Unconventional Upstream Data Model. The project initially sought to establish a standards-based Production & Operations Model for the following functions: Support for a federated data environment Interoperability Remote Surveillance (event based surveillance) of field assets throughout the enterprise, leveraging collaborative technologies such as Microsoft SharePoint and enabling contextualized views of the information via W3C standards-based semantic technologies Analysis Field reads Daily reports Historian - time series data and calculations Enterprise-wide computerised maintenance management system (ecmms) Downtimes, defect elimination, over and under production Run hours to ecmms (preventative maintenance triggering) Usage readings out of the historian to the ecmms Standards for other functions will be developed in the longer term: Capacity and distribution planning Production management, including contracts/nominations, accounting, balance, reconciliation, materials tracking (movements, inventories, batches, qualities) models, simulation and line-pack. Regulatory, compliance and environmental reporting Energy trading and risk management Operations monitoring: control performance, well performance, asset performance and operator performance Water treatment plant sample management Reservoir management Field/maintenance changes to plant/facility engineering Operations & maintenance (O&M) asset configuration updates i.e. changes to procured assets Early warning notifications based on condition based monitoring (CBM) determinations Asset lifecycle support, including integration of engineering and construction data with operations and maintenance support systems. It is evident that standards such as ISO15926 are central to the construction of this solution (refer to Figure 10 on page 13 for discussions regarding the relevance of ISO15926). 5.2 Outcomes The exercise identified that the standards incorporated by OpenO&M 4, ISO15926, Energistics [primarily PRODML 5 ], and ISO14224 have the most relevance to Unconventional Upstream Data models. 5.3 Proposed Industry Standards-based Executive Semantic Model Figure 9 on page 12 shows the CSG Data Model developed using the Semantic Model to accommodate Supply Chain and Operations Monitoring requirements and open industry standards such as ISO OpenO&M is an initiative of multiple industry standards organizations to provide a harmonized set of standards for the exchange of Operations & Maintenance (O&M) data and associated context PRODML (Production Markup Language) is an industry initiative to provide open, non-proprietary, standard interfaces between software tools used to monitor, manage and optimize hydrocarbon production.

12 Unconventional Upstream Data Model - Turning Data into Knowledge 12 : Collaborative Performance Management Visualisation Contextualised Model Views Orchestrated Workflow, Notifications & Escalations Analysis, Performance Metrics & Reporting Content & knowledge Management Composite Applications E.g. Worksite Transfer Operations Management Optimised Production Management Available System Capacity Operational Decision Support Any other Applications Web parts SharePoint Semantic Pipe Model Change Service Event Notifier RDBMS e.g. Oracle/ MSSQL Production Accounting Or Service e.g. MPI ERP Or Service e.g. SAP CMMS or Service e.g. eams SAP-PM DMS or Service e.g. Documentum GIS or Service e.g. ArcGIS MS Office Application, or Service e.g. Excel Historian or Service e.g. PHD LIMS or Service e.g. LabWare CEMS or Service e.g. Sick Land & Environment or Service e.g. Borealis Process Optim. Or Service e.g. UniSim RBMI or Service e.g. GP Allied PTW Or Service e.g. evision Prod & Ops Model TM Federated Model A Virtual RePository The Ontology ISBM server of objects ISO15926 server of objects OPC UA server of objects ProdML server of objects ISA-95 server of objects ISO14224 server of objects (any) server of objects SOAP/HTTP SOAP/HTTP SOAP/HTTP SOAP/HTTP SOAP/HTTP SOAP/HTTP SOAP/HTTP ISBM endpoint/ datasource ISO15926 endpoint/ datasource OPC UA endpoint/ datasource ProdML endpoint/ datasource ISA-95 client endpoint/ datasource ISO14224 endpoint/ datasource (any) endpoint/ datasource Standards-based Interoperability Environment Engineering Data Management Application Plant & Process Systems Oil & GAs Production Management & Optimisation Applications Operational Views Reliability and Maintenance (RM) Applications Any other Application supporting Standards-based Interoperability CCOM ISO15926 OPC UA ProdML ISA-95 ISO14224 (any) Model OpenO&M Common Conceptual Object Model Engineering Resource Master Real-time Operations Master Production Flow Network Operations Resource Hierarchy Asset Tamplates Other Figure 9: Proposed Industry Standards Ontology for Production Model Industry Standards

13 Unconventional Upstream Data Model - Turning Data into Knowledge 13 Each data source historians, relational databases and web services has its own language. Executive s (IX) data access service and semantic adaptors therefore consume data from these data sources and harmonise it into a cohesive entity a virtual repository that can be queried by dashboards, designers, and external systems. The introduction and application of industry standards greatly increases the range and depth of available data and the ability to access and query it directly through IX. The servers or objects shown above do not exist today. It is envisioned that the system will at some point in the future speak to standards-based endpoints, or services, directly. 5.4 Workshop Model Example for Interoperability Standards are now being more frequently rendered in a semantic format according to the Web ontology Language (OWL) authoring of the Resource Description Framework (RDF) described by the W3C. As IX s Semantic database accurately leverages semantic standards and SPARQL as a query language, and conforms to the W3C recommended open standards, IX can integrate this information in its natural format, without copying any data to render meaningful views of blended data for reliability, petroleum, production or process engineers, or indeed most other roles that have a need to view information contextual to their role, typically from multiple data sources. An example workflow of engineering material data from capture through to the operation and maintenance stages is depicted below in Figure 10. Figure 10: ISO15926 Data Lifecycle for CSG Production Models. Most life-cycle orientated engineering design systems output their engineering design sheets or P&IDs in a data exchange standard called ISO15926, IX can provide a more efficient means of exchange between the engineering construction and material data, and the asset master data register (e.g. SAP-PM or Oracle eams) by supporting interchange between each in a federated data environment. Importantly IX closes the loop to act and retain knowledge. Given typical CSG expansion programmes to scale assets from a nominal current state of 30,000 assets to over 300,000 in the next five years this model could result in significant savings throughout project construction phases and the operations and maintenance lifecycle of assets

14 Unconventional Upstream Data Model - Turning Data into Knowledge 14 6 Conclusions & Recommendations CSG producers face significant challenges to improving operations and production results but industry leaders are turning to innovative technology based on data modeling and collaboration tools to overcome these challenges and turn data into knowledge. The combination of Honeywell's standards-based approach and advanced Executive software technology can make this a reality. 6.1 Step 1: Think in Standards We must plan to represent the Unconventional Upstream Data Model using predicates from industry standards..when we think in standards we will have a model that uses standard predicates and is ready to communicate with external systems. However, the system will not yet have the full complement of tools and functionality to actually communicate with those systems. The following standards seem most practical to apply: ISA-95 ISO CCOM ISO14224 ProdML, and OPC-UA 6.2 Step 2: Talk in Standards Once we have represented s CSG Production Model concepts using predicates from a standards based Ontology we can begin to implement service level interfaces to external systems. Figure 9 shows a number of potential servers of objects: we could implement OpenO&M s Information Service Bus Model [ISBM] interface to expose the CSG Production Model to ISBM-based consumers, for example. Those consumers would not know that the ISBM server they are talking to is backed by a semantic model or has predicates for ProdML, PPDM, or other standards. However, they would be able to use service level methods that allow them to interact with the CSG Production Model as simply as if it were an ISBM producer. This means systems not only communicate via standard connectivity methods, but also exchange complex data sets that describe a composite relationship about, or between, assets of production information; better known as a concepts. This is true systems Interoperability at work and significantly reduces the inherent difficulties of integration and the overhead of data synchronisation.

15 Unconventional Upstream Data Model - Turning Data into Knowledge 15 For More Information Learn more about how Honeywell s Coal Bed Methane solution can enhance production, visit our website or contact your Honeywell account manager. Honeywell Process Solutions Honeywell 1250 West Sam Houston Parkway South Houston, TX Honeywell House, Arlington Business Park Bracknell, Berkshire, England RG12 1EB UK Shanghai City Centre, 100 Junyi Road Shanghai, China WP ENG September Honeywell International Inc.