Maximising value from seismic using new data and information management...

AEGC 2019: From Data to Discovery – Perth, Australia 1

Maximising value from seismic using new data and information management technologies Jess B. Kozman* Matthew Holsgrove Woodside Energy Ltd WIPRO 11 Mount Street, Mia Yellagonga Level 2, West Wing, 3 Sheldon Square Perth, WA 6000 London W2 6PS, United Kingdom [email protected] [email protected]

INTRODUCTION

The development and commercial availability of cloud data storage and delivery technologies in the last two years has led to increased interest in transcription and re-baselining of large

volume seismic survey data to the cloud. Oil and gas operators now look at advantages of having large volumes of subsurface digital data in a cloud environment. These advantages include; lean development of new software using large amounts of compute cycles on short notice, an agile and “fail fast” culture of innovation, collaboration and acceleration, avoiding large capital outlays and long project timelines, and running multiple initiatives in parallel with scalable compute, memory, bandwidth and storage capacity. Commercial cloud provides best practices in cyber security, intrusion detection, identity and access management, performance and capacity planning analytics, connectivity across high bandwidth backbones, and backup, disaster recovery and business continuity through Service Level Agreements. This means internal IT can transfer these costs and risks to cloud providers. In cases such as blockchain auditing and advanced tagging for entitlements, newer solutions assume inherently cloud-based and developed platforms. Recently some of the barriers to oil and gas operators using cloud storage for large seismic volumes have been offset by emerging advantages. Legacy seismic data volumes are large compared to typical on-premises disk storage, and the ability for edge computing to put data next to available CPU and GPU capacity has reduced the impact of transmission latency for even multi-terabyte volumes (Figure 1).

Figure 1. A cloud data manager’s view of seismic data in a GIS Feature Class, showing geospatial data objects for multi-client data and geographic locations of physical and cloud storage for an operator’s seismic data. The final volume of seismic data stored in the cloud of 23.3 petabytes for Woodside will be the equivalent of 199 years of streaming HDTV content. Oil companies recognize that seismic volumes and derived specialized processing and quantitative interpretation products are highly proprietary and represent significant competitive advantage. As seismic is used in operationally intense unconventional plays such as shale and coal seam gas, some companies working on open data platforms have seen faster growth and improved valuations over those who continue to

SUMMARY Geophysical data is one of the key decision support factors for successful discovery of hydrocarbons, and operators have multi-decadal inventories of survey data in key basins. Woodside has a history of exploration in Australia from 1954 and seismic data from as far back as 1964 for the Northwest Shelf and other global exploration areas. Since 2017, Woodside has been developing a cloud based Next Generation Seismic Data Management platform that will allow end users to extract maximum value from legacy and modern data. The platform is open and vendor agnostic for storage platforms and applications and services consuming data. It uses industry standard and public domain standards for data structures, APIs, and transfer protocols, and streamlines access and delivery of seismic products across the data lifecycle, from acquisition to regulatory submission and divestiture. In 2019, Woodside data management received executive approval to accelerate data re-baselining to have all Woodside seismic data transcribed from magnetic tape media into cloud storage, while enabling direct cloud delivery of acquired data. At completion, this project will have handled over 100,000 individual pieces of storage media, over 200,000 documents, 650,000 individually indexed data objects representing 54,000 2D lines and 400+ 3D surveys, over one trillion seismic traces, and an additional 200,000 spatial data objects representing available multi-client seismic data. The total volume of data from tape exceeds 20 petabytes, a volume nearing that kept on tape by the Curtin Institute of Radio Astronomy as a precursor to an instrument that will at full capacity require data links for volumes equivalent to all internet traffic. The primary conclusion of the project work is that cloud storage methodologies have enabled all the goals of the project and led to additional value and benefits beyond the anticipated scope. Key words: seismic, data management, cloud, technology.

Maximising value from seismic Kozman and Holsgrove


hold their data in proprietary data centres (Cann, 2018). Resistance to using a single provider for any application, including seismic interpretation and storage, and “hidden” egress costs have led to poly-cloud solutions as cloud storage becomes more commoditized with competitive pricing. Where operational data from equipment in the field is in historian-based time series data volumes, subsurface digital technology can leverage contracts and volume discounts established for operations. All of this builds a business case for storing seismic data volumes with a mandatory set of standardized metadata in cloud storage, with an industry standard Application Programming Interface for delivery to end users. Capability maturity modelling has now identified risks associated with not having geophysical data available in cloud storage. These include the costs of operating a proprietary data centre for exponentially growing volumes versus leveraging economies of scale with a commercial provider with global reach and evolving services. Using global data centres is cost effective for an operator like Woodside, domiciled in Perth with limited bandwidth but with exploration interests and partners in global locations. Operators also risk eroding skills and capabilities if they do not embrace cloud technologies, with computer science and information technology graduates competing for roles in oil and gas industry that did not exist a few years ago, and competition from other industries for the top talent required to manage large volumes of data in the cloud. Software that requires data on local disk will also have their value depreciate rapidly as seismic functionality migrates to cloud native services. Companies with sunk costs in a large portfolio of proprietary or bespoke code developed and locked into specific compute platforms and environments will start to experience increased costs for upgrades and maintenance, leading to stranded investment, business risk, and reduced functionality. Finally, one of the biggest drivers for storing large volumes of technical data on enterprise scale cloud storage platforms is access to new and innovative business models outside of those already leveraged in the product and process areas. Some recent studies have set the oil and gas industry’s digital capability level as low as 3rd quartile compared with other asset intensive industries, “setting the stage for enormous competitive advantage for those who move quickly on technology adoption and innovation” (Mancini, 2017). Woodside for example is now recognizing the benefits of performing Full Waveform Inversion in the cloud, reducing the time for delivery of final imaging from 18 months to as little as 4 weeks (Pearce, 2017).

METHOD AND RESULTS

3D seismic data has the largest impact on reducing the uncertainty during exploration, appraisal and development (Farris, 2012), and there has been increased interest in best practices for moving these large volumes of data to multi-tiered cloud storage, so operators can benefit from techniques such as automated data mining and extraction, semantic mapping, data analytics, and machine learning. As early as 2015, Woodside had highlighted benefits of cloud computing and storage, defining use of cloud services in support of a global strategy of increased efficiency, capability and growth.

Figure 2. Woodside’s 2015 definition of areas of management required for different cloud models showing a preferred solution for Software as a Service (SaaS) where Enterprise Public Cloud vendors were responsible for data storage (grey boxes), over other models where Woodside would manage data (blue boxes) Woodside also identified the risk of failing to leverage new data and information technology solutions coming to market through cloud deployments as quickly as peers and competitors, including rework costs and longer times to delivered value. Net present value was especially important in a business environment focused on delivering production to large, capital intensive LNG investment projects with long time frames in domestic markets in Australia. Woodside proposed a cloud first strategy in which all new IT solutions and services would be required to utilise cloud services unless it could be shown that this would significantly increase overall cost of ownership, provide an inferior experience or business outcome, and could not provide appropriate risk controls. SaaS, where the enterprise cloud provider managed data, was defined as the preferred model (Figure 2). Much of Woodside’s legacy seismic data was stored on magnetic tape media in a Perth warehouse. Woodside selected a third-party vendor with the capacity and expertise to deal with legacy media types from several decades of seismic acquisition. The vendor also delivered an industry-standard data model for associated metadata and spatial information, and scalable throughput depending on changing business priorities and demand. The Woodside strategy was deliberately multi-vendor on public enterprise cloud with virtualised on-premise services, to reduce technical debt and improve support for acceleration, collaboration and innovation. Seismic data was an early target for this strategy, eventually accounting for over 90% of the volumes stored on cloud. Indirect benefits will be realized through consolidation of commercial agreements, centralised governance and service management, reduced duplication of services, and enablement of enhanced data integration and transfer among solutions. For seismic data the platform had to protect enterprise level geophysical data with security, regulatory and functional requirements for confidential and mission critical information, commissioned and used globally. Petabyte scale seismic volumes accrued benefits quicker based on shared infrastructure and economies of scale, while transferring typical upgrade and lifecycle costs for storage to an outside vendor. A consumption-based cost model encourages a “fail



fast” agile environment and provision of capacity on demand, while mitigating risks of country-specific data sovereignty laws and service providers with access to data storage. Business owner approval has been obtained for entitlements requirements and the metadata index uses industry-standard definitions for effective and expiry dates of ownership, data submission, and licensing restrictions at the data subset-level. The index is created and maintained using a system which has been certified to be 100% compliant with the Professional Petroleum Data Management version 3.9 Data Model, so the index is fully transportable to any other application using the same standard, and definitions and relationships used in the metadata model are in the public domain (Malajczuk, 2018). Within Woodside, the management of geotechnical data up to and including Confidential Level, which includes data shared with named individuals and groups, is a supported use case on public enterprise cloud, and the entitlements requirements align with citizen data science principles that data should be accessible by default and restricted by exception. A comprehensive view of the vision for a Next Generation Seismic Data Management solution is shown in Figure 4. It included providing a single point of access for validated field, temporary, and final loaded and interpreted seismic volumes, milestone projects, reports, velocity volumes, navigation, acquisition, observer and processing support documents, quality, entitlement and source quality and confidence flags, all tied to map views in native applications used by geophysical staff. This vision has been realized and in fact improved upon through the utilization of multi-tiered cloud storage for large seismic data volumes.

Figure 4. Data types available in the Next Generation Seismic Data Management solution. One of the first steps in creating a Next Generation Seismic Data Management solution was to automate and improve existing data management processes. In 2016, Woodside performed as assessment of the data loading process to support the business case for centralized tape management and a spatially enabled catalogue of all enterprise seismic data. The original business objectives of the project were agreed and shared with a geotechnical operations and subsurface assurance group. A restructuring of the organisation led to a re-design of the seismic loading process, including the incorporation of a “OneList” seismic workflow development in the corporate workflow platform supported by the digital organization. This list provided an audit trail for seismic data volumes from the time they were ordered by the responsible asset teams until archiving in the Woodside Project Archiving and Retrieval System, which took previous versions and milestone copies of projects and moved them to lower tier storage with an associated set of standardized metadata.

Figure 5. Seismic data lifecycle steps documented and traceable in the “OneList” JIRA based audit trail. By 2016, the project had assessed internal and external data loading and the initial target area for cloud storage, while further validating the business case for a third-party tape management and transcription service and a roadmap for delivery of a spatial catalogue of Woodside’s seismic inventory. The scope of the geospatial index had been determined to include final seismic volumes, intermediate processing products, field data, acquisition and processing reports, velocity models, navigation, support documents and files, quality and confidence ratings, and a link to the physical or digital location of trace storage. Optimizing an existing queue for seismic data loading delivered an improvement of 60% in 2017 to 490 seismic data loading jobs for the year, with less than one rework request per month. In 2017 the seismic index was released as a plug-in to the core interpretation and modelling application and from within the internal GIS system (Figure 6), with the ability to “point and click" from a map view to a listing of all available seismic data and associated documents in Woodside priority areas. A decision was taken to load seismic data to public enterprise cloud, and to enable ordering of seismic data from the inventory for loading with specifications for geospatial sub-setting in time or depth domains and decimation of pre-stack gathers. The team also worked on improvements to the plug-in to the core interpretation product to ensure that users could effectively search and filter on the growing catalogue of seismic volumes available in any given geographic area without having to deal with a cluttered map view.

Figure 6. An example of a GIS map layer showing the global distribution of multi-client survey data available to Woodside interpreters (Navigation data courtesy of Canesis, GeoEx, TGS, and Spectrum)



By the end of 2017, geophysical data under management by the Digital Subsurface team included metadata for all historical and legacy data. This provided links from a map to digital versions of reports (Figure 7).

Figure 7. A photo of the physical record for one of the earliest geophysical datasets under management by Woodside, accessible through a search of metadata. The aeromagnetic survey is from 1954. By early 2018, the program was already demonstrating quantifiable and measurable benefits from access to seismic data in the cloud environment. The first of many success stories centred around streamlining the process of transferring a 200+ Gb seismic data volume from the Woodside Perth office to the subsidiary office in London. Existing FTP based processes would have taken up 57 hours and the business had been looking at the possibility of sending a USB drive with an employee who was in transit. Instead, cross-region replication was used to transfer the volume and the data was available to the interpreters in the U.K. by midnight the same day. Interestingly, because of the lack of bandwidth between Perth and the other capital cities in Australia, less than 4% of the shorter delivery time was spent in moving the data over the nearly 14,000 km between the cloud data centres in Sydney and London, while the remaining 96% was consumed by low-bandwidth connections between Perth and Sydney on one end, and then between the cloud Point of Presence in London and the Woodside office in London. This was because although the two buildings are physically close (literally across the street), no large bandwidth telecom connection exists to the older building housing the Woodside offices. The subsurface data management group was able to recognize up to 59% cost reductions for cloud transfer by eliminating multiple tape transcription, physical shipping, standby time, and additional vendor costs for a dataset being copied to a joint venture partner for reprocessing, by having their high-performance computing centre access the subset of data from cloud storage. Woodside is also looking at reprocessing agreements in which third party vendors include the cost of retrieval from tape and transcription to cloud in return for access to open file data for which Woodside already has tape media, saving them the cost of multiple downloads from the government regulator. Late in 2018, an initial showcase and deployment of the GIS interface provided benefits almost immediately when a user was able to use the ArcGIS portal map interface only 68 minutes after its go-live to use a geographic search to identify

four 2D processed records in a new business priority area. By the end of 2018, seismic data users were routinely compressing timeframes for access to data from weeks to days, in some cases from days to hours, and the data management group was able to put together a business case for individual asset teams showing that the capital expenditure required to move and transcribe digital data from tape to cloud would pay out in a positive return on investment in anywhere from 5 to 14 months depending on the volumes and usage patterns of the data. The benefit was accrued from eliminating the costs of managing tape and reducing the total cost of ownership for storage by as much as 36% from on-premises network attached disk arrays. In 2019, Woodside authorized the acceleration of the Seismic Tape to Cloud project to achieve the ambitious goal of having the last piece of physical seismic tape media out of their offsite warehouse by the end of the calendar year. This involves capital expenditures for tape retrieval, reading, transcription, indexing, output to cloud storage, association with support documents, and creation of a geospatial index, plus recurring operational expenses for cloud storage. The project is tracked and reported in terms of the number of physical media items with trace data in the cloud made available in a map view to end users (Figure 8).

Figure 8. Graph showing progress over time of moving physical media to the cloud, with milestones and benchmarks for QC and visibility on mapping applications. The business case for the program is built on three factors. First, elimination of annual physical tape management costs of multiple hundreds of thousands of dollars per survey, depending on the activity level of the asset teams. Second, time savings for access to data for business-critical decisions from weeks to hours, and in some cases from days to minutes, once seismic volumes are accessible in the cloud. And third, reduction of risk. An audit of the tape inventory showed that the project would safeguard data that was currently beyond its nominal shelf life according to manufacturer’s and media specialist guidelines (Diviacco et al., 2015). The acceleration program is set up to move data from magnetic media in warehouse storage all the way through a metadata extraction and indexing workflow until it is fully map visible to end users, and to provide links to other support data in document management systems. It also allows users to compare data in the Woodside inventory with available data from either multi-client vendors or open-file sources.



Woodside established in 2018 a workflow with seven different multi-client vendors who provide more than 90% of the multi-client data purchased by Woodside, to provide updates of their multi-client global libraries with a standardized set of attributes so they can be merged geospatially with quality-checked seismic navigation in the Woodside inventory. This will also allow Woodside to perform analytics on the timing and availability of data in key areas of business interest. The priorities for tape transcription are based on business priorities to align with Woodside core strategy and vision (Figure 9), providing a mixture of media types to keep tape transcription services running at full capacity, and a program to take older data at risk off magnetic media which has exceeded its shelf life.

Figure 9. A heat map generated to show the relative level of activity of media re-baselining by geographic areas. The system is now sufficiently mature to support an industry first for an international operator, direct to cloud transmission of field acquisition data from the offshore vessel to the data centre, without tape delays or costs. This workflow is expected to be implemented less than 12 months after first being demonstrated successfully live at an industry event (Beyer, 2018), showing how quickly new workflows can be implemented and accommodated when an adaptable and scalable platform is in place.

CONCLUSIONS

The Woodside Next Generation Seismic Data Management program has shown that quantifiable and measurable benefits to the business can be recognized and demonstrated by having large volumes of seismic data stored and accessed from enterprise cloud storage. Work remains to be done to fully understand the impact of initial choices on bucket and file structures, tagging, and formats, especially for pre-stack data. We believe that previous objections or concerns around security or access have now been resolved by international operators, and that more organizations will be implementing programs which provide for point-forward workflows

delivering seismic data direct to cloud storage. Woodside is leading in demonstrating that benefits can be derived from this strategy, as measured by reductions in cycle time, cost, and risk, and value added to business-critical subsurface decisions based on subsurface geophysical data.

ACKNOWLEDGEMENTS The primary authors wish to acknowledge contributions from technical teams at Woodside, as well as technical partners such as Amazon Web Services, DataCo, ESRI, The Information Management Group, Interica, Katalyst Data Management, New Digital Business, Schlumberger, and WIPRO, among others.

REFERENCES Beyer, M., 2018, TapeArk Demos an Exploration First, Business News West Australia, 23-Apr-2018, https://www.businessnews.com.au/article/Tape-Ark-demos-an-exploration-first Cann, G., How Cloud Computing Will Transform Oil And Gas: Digital Oil & Gas, 15-May, 2018: http://digitaloilgas.com/cloud-computing-transform-oil-gas/ Diviacco, P., Wardell, N., Forlin, E., Sauli, C., Burca, M., Busato, A., Centonze, J., Pelos, C, 2015, Data rescue to extend the value of vintage seismic data: The OGS-SNAP experience, GeoResJ, Volume 6, 2015, Pages 44-52, ISSN 2214-2428, https://doi.org/10.1016/j.grj.2015.01.006. (http://www.sciencedirect.com/science/article/pii/S2214242815000078) Farris, A., 2012, How big data is changing the oil & gas industry: Analytics, November/December 2012: http://analytics-magazine.org/how-big-data-is-changing-the-oil-a-gas-industry/ Malajczuk, S., 2018, Metadata Migration to the PPDM Model: A ‘Good Practice’ General Methodology, Lessons Learnt and Case Study Examples, Professional Petroleum Data Management Association, 15-Nov-2018: https://ppdm.org/ppdm/PPDM/events_list/Past_Events/PPDM/Past_Events.aspx Mancini, J., 2017, Data is the New Oil – Especially in Oil and Gas!: M-Files Blog, 10-May-2017: http://www.m-files.com/blog/data-oil-oil-gas/ Pearce, R., 2017, Woodside pushes cloud’s limit to crunch data: ComputerWorld, 30-May-2017: https://www.computerworld.com.au/article/619933/woodside-pushes-cloud-limit-crunch-data/

Maximising value from seismic using new data and information management...

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