The oil and gas industry utilises state-of-the-art geospatial technology in its entire workflow, from the inception of an exploration idea, to all the way through to consumption of hydrocarbon products at a petrol station, burner tips or as chemical feedstock at a manufacturing plant.
In the 1890’s a purely manual geospatial workflow created a map showing 31 natural oil seeps in Borneo that locals had hand-dug for lamp oil. In 1907, this same map was carried by hand to the offices of an oil company in London. A branch manager in the office liked the map, and three years later Royal Dutch Shell was drilling into what would be the first oil field in Malaysia. These early maps used hand-drawn geologic field observations to support the geospatial oil and gas workflows that are still used today to explore for a new oil field.
Today, as every major petroleum company such as Royal Dutch Shell, Exxon Mobill, BP, Mubadala Petroleum continue to explore for oil and gas in geologic basins around the world, automated geospatial workflows start at the early frontier and basin exploration stage for each of them, bringing geologic and geophysical data together in desktop GIS tools and geoscience interpretation applications. In the case of integrated energy companies like Qatar Petroleum, they can extend all the way to delivery of compressed natural gas to power a metropolitan taxi fleet, using real-time GPS and thematic mapping tools for fleet logistics.
The most visible of these uses is in the maps that drive prospecting and exploration, or what the industry refers to as ‘upstream’. Almost every geologic structural modelling, petrophysical analysis, rock mechanics, or geophysical interpretation package has a built-in geospatial mapping function, and many oil and gas applications also provide plugins or links to GIS database products.
Prospecting and exploration for new areas in which to search and drill for oil and gas requires bringing data together on maps from many different disciplines. The plotting of existing wells on maps has become more complex with the advent of technologies such as multiple wellbores from a single surface location and long-reach horizontal drilling. State-owned oil companies like the Mexico’s PEMEX report 80% reduction in data capture times and 60% improvements in data quality from using GIS-enabled oil field management system to map their existing infrastructure.
Geophysical seismic data has evolved from two dimensional lines that provided a regional perspective to today’s densely sampled three-dimensional ‘cubes’. Exxon was an early leader in using industry sub-surface mapping tools and 3D seismic data to determine reservoir volumes and extents, and still realises cost savings today by avoiding investments in non-productive acreage, costly dry holes, misplaced platforms, incorrect assumptions on reservoir extent and geometry, or premature field abandonments.
Big data, big solutions
Understanding and integrating different kinds of data in a geospatial workflow can be challenging, and many oil and gas companies employ GIS experts, surveyors, cartographers, and geodesists. With regional projects, issues such as coordinate reference systems and datum shifts can become important. A translation of 112 metres on Google Earth may be only a nuisance to the average tourist, but the same error in the position of a multi-million dollar well can reduce the profitability of an oil and gas company. Oil and gas geotechnical workers must be able to use coordinate conversion libraries for recommending the placement of exploration wells. Many lucrative fields have been missed because of errors in surveying or map datum conversions.
Exploring for oil and gas also requires large volumes of geospatial data, with upstream exploration and production using multiple terabytes of spatially located data. As an example, BP has opened the ‘world’s largest supercomputer for commercial research’, with a capacity to use 23.5 petabytes of geospatial data to enable proprietary petro-physical rock properties and fluid flow modelling packages directly from geospatial high-resolution 3D images.
To give an indication of the volume of data petroleum companies deal with, Qatar Petroleum land seismic crews collect 83 million data records per day over 860 sq km, creating a final volume of 700 terabytes of geospatially referenced data over a single oil field. In the North Sea, BP has been generating 70 terabytes per survey over their field in a continuous operation since 2003. Once a field starts producing, geospatial data can continue to come in from maintenance operations. Chevron has sponsored a project to harvest geospatial data from unmanned submersible vehicles. Real-time pattern recognition tied to geospatial coordinates is also used to monitor the security of oilfield facilities by watching for intrusions along the pipeline right of ways or identifying potential hostile actions in the vicinity of offshore oil rigs. The Kuwait Oil Company has implemented a fully GIS-enabled integrity management system for pipelines destroyed and damaged in military invasions.
After an oilfield is found, operators still have to collect and transport the product. A geospatial view of collection, gathering and distribution pipelines shows the variety of data types in a geospatial view, from real-time environmental sensors at well locations to financial spot pricing at retail outlets. A GIS geo-database system at the Malaysian national oil company Petronas will have thousands of layers used by multiple technical users, from reservoir engineers to HSE auditors. The pipeline, or ‘midstream’ part of the oil business, also uses advanced sensors and network technology. Geo-rectified satellite imagery from subscription vendors plays a role in site planning for exploration survey work, and in pipeline operations for geospatial workflows to determine environmental impact of facilities, pipeline corridors, or emergency and hazard response.
Mapping pipeline routes can require integration of paid subscription information, public domain sources, proprietary company data, competitor business intelligence, and knowledge from organisational experience in the area. For instance, Tullow Oil has documented up to 15% cost savings in using GIS routing systems for pipeline planning. Often a competitive advantage in a market driven by commodity prices can be gained by delivering the right geospatial data, to the right person, at the right time. At Mubadala Petroleum, for instance, multiple layers of exploration and production geospatial information are delivered to end users over a networked desktop GIS system with a query-based table of contents. This allows users to very easily answer questions like; “what scouting data do we have within 20 km of our recently completed pipeline?”
The scope of this data has led other disciplines within petroleum organisations to ask how they can glean value from geospatial workflows, including human resources and health, safety and environment (HSE). Shell has pointed out that increasing attention to unconventional resources such as shale gas and coal bed methane is adding to the types of geospatial data that need to be processed and analysed. These new data types can include high-resolution images from well logs, videos of fluid flow from hydraulic fractures, acoustic data from down-hole drilling and completion processes, and unstructured text from field operator’s notes on mobile devices. These data types can be accessed from GIS enabled Web portals, and fed into geospatial workflows to support business decisions.
In the operations of pipelines and production facilities, geospatial workflows have a much more direct impact on operating profits and revenues. For example, geospatial workflows using demographic and spatial analysis can be applied to decisions about how much of aging natural gas distribution networks under major cities should be replaced in a given year. Without accurate geospatial workflows to present information about proximity and routing, attempts to assess the risk and set priorities could be misinformed.
Oil and gas operations that depend on geospatial workflows
While many oil and gas companies only follow the value chain as far as refining and processing, others are integrated energy companies and run their own retail outlets for fuels. This is known as the ‘downstream’ end of the business. Many national oil companies and global super-majors fall into this category, and are the brandnames that can be seen at petrol stations. These companies continue to use geospatial data with their products, and many are exploring alternative energy resources. So, an integrated energy company such as Iran Gas Company might use geospatial workflows that overlay multiple thematic layers to evaluate the business potential and competitive environment for wind energy in the same area that they are marketing natural gas. Total Energy also uses geospatial workflows with data retrieved by satellite from GPS-located ground stations to evaluate the potential solar energy available in its SHAMS concentrated solar power field in Abu Dhabi.
Oil and gas companies with global integrated operations such as Saudi Aramco may use workflows in GIS desktop tools for 3D proximity analysis and buffering to bring together demographic, transportation and infrastructure maps to determine the best location for a new petrol station or convenience stores in locations such as China. ExxonMobil can enable workflows using advanced 3D smart city technology to perform urban design simulations and visualise different scenarios for efficient location of their Esso petrol stations in Singapore.
Oil and gas geospatial data managers realise that their industry shares many challenges and approaches with other geospatially data intensive industries, such as aerospace, military intelligence and climate modelling. However, the range of contexts in which geospatial workflows are enabled are much broader. This is reinforced when GIS forums allow oil and gas GIS professionals to recognise that many of the geospatial workflows presented by the GIS users in government agencies and e-commerce will not scale to the environment of deliberate uncertainty that pervades oil and gas exploration. The ability to track, analyse and manage large datasets that support global oil and gas exploration efforts continues to improve with increasingly complex geospatial workflows.