Giant’s quest for power

National oil giant Saudi Aramco, which controls a quarter of the world’s oil reserves, has invested heavily in IT products and services to ensure that the oil keeps flowing.

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By  Peter Branton Published  October 28, 2006

National oil giant Saudi Aramco, which controls a quarter of the world’s oil reserves, has invested heavily in IT products and services to ensure that the oil keeps flowing.

Aramco has applied for patents on technology it uses in oil exploration, such as 3-D seismic volume interpretation visualisation techniques.|~|When writing about the oil and gas sector in the Middle East, one name stands out above all others — Saudi Aramco.

The national oil company for Saudi Arabia, Aramco is a giant firm, responsible for virtually all the Kingdom’s hydrocarbon enterprise — which means it controls a quarter of the world’s oil reserves.

The Ghawar and Safaniya oilfields that it controls are, respectively, the world’s largest onshore and offshore fields, and it runs one of the largest fleet of supertankers in the world. The firm is also one of the largest producers of gas and natural gas liquids (NGL).

To keep the oil flowing in such quantity, Aramco is unsurprisingly one of the leading investors in IT products and services in the Middle East. When IT Weekly contacted Aramco for information about how it used IT the firm sent a detailed a response; outlining a number of key areas where it used technology to help.

IT was described as “an enormous area of endeavour” for Aramco, encompassing a huge portion of its exploration and producing activities.

For instance, smart well systems and down-hole sensors are part of a larger strategy to develop intelligent fields, an approach that combines real-time monitoring and timely reactions to changing well and reservoir conditions to optimise production and reservoir management, Aramco said.

A study conducted by Cambridge Energy Research Associates, in conjunction with oil and gas companies, suggests that the intelligent field concept could significantly improve recovery factors, reduce capital expenditures, and reduce downtime and operations costs.

Aramco also provided detail information on how it uses IT to help in seismic processing.

The oil giant has applied for patents on technology it uses in this field, which is extremely demanding of computing power: in 2005, the company migrated the entire conventional seismic processing environment from a proprietary IBM supercomputer to more cost-effective Linux clusters with more than 600 Tbytes of storage.

Saudi Aramco’s 3-D visualisation centres have undergone constant refinement since their inception, and in 2005, the centres were upgraded to the latest digital technology.

New visualisation techniques were developed for exploration and production, including seamless data integration between processing and interpretation, and super-large 3-D seismic volume interpretation.

Seismic surveys have become the most common and widely used method for studying earth layers.

Today, sophisticated oil exploration technologies use devices that operate on principles similar to those of earthquake measuring scales, such as seismographs to measure and record lower frequencies of ground vibrations or man-made shocks produced by blasting explosive charges in a series of small depth-holes to generate vibration waves.

“Most of us are familiar with ultrasound pictures known as sonograms,” Aramco’s spokes- man explains.

“We build these images by sending sound waves into the human body and recording their reflections, to see the baby before it is born. The objective of seismic data is quite similar; not to image the belly of a human being but rather to image the belly of the earth. We want information in respect of sub-surface structures, before we drill, by having sonograms of the earth. And we do that using sound waves as well.”

Processing seismic data is important to improve the clarity of these pictures and one of the fundamental steps of seismic data processing is fractal deconvolution. It is akin to developing photo film, to add focus and enhance the resolution.

“Saudi Aramco has developed a new deconvolution algorithm, called fractal deconvolution that yields more accurate seismic data. The top seismic section was processed by the conventional method. You can see several breaks in the data because of the lack of focus and resolution. The lower section was processed with our new method,” the spokesman says.

“This method improves the focus and resolution and allows us a better view into the subsurface. By having better pictures of the subsurface, we can enhance our success in looking for hydrocarbon resources.”

“One of Saudi Aramco’s objectives is to be able to tell something about the quality of the reservoir before we drill, just like seeing the baby before it is born.”

“For example, we would like to ascertain the locations of sub-surface channels and target them, because they hold rocks the best reservoir quality. These channels are not migration paths — they are pay zones within the reservoir — and they are quite deep, about 14,000 feet below the surface,” he adds.

Coherence analysis allows Aramco to detect these channels. It has developed a new algorithm for computing coherence which yields more accurate images of the subsurface channels, when compared to conventional methods.

Aramco not only wants to detect these channels but to estimate their thickness as well.

The thicker the channel, the bigger the payday is. Saudi Aramco has developed a new algorithm, based on analysing the variability of the frequency content of seismic data.

This allows it to estimate the channel thickness and produce images that depict not only the locations of sub-surface channels, but also their thickness. Therefore, Aramco can target the thickest portions of the channels and maximise well productivity.

Artificial neural networks simulate biological ones with the objective of replicating the human’s ability to do pattern matching, pattern identification, and information extraction.

Saudi Aramco utilises neural networks to extract porosity information from seismic data (briefly, porosity is how much void space the rock has, how sponge-like the rock is, and therefore how much hydrocarbons it can hold. The higher the porosity, the better the reservoir).

“We utilise neural networks to extract porosity information from seismic data and build porosity models of the reservoir that we then send to the engineers to do reservoir simulation,” the spokesman says. Saudi Aramco’s contribution was in the development of a new type of neural network, called regularised neural networks, which are much more accurate than the conventional ones.

Reservoir modeling and simulation is one of the most important tools for conducting proper diagnostics and forecasting. In response to the simulation requirements of its giant reservoirs, Saudi Aramco has developed a reservoir simulator, the parallel oil water reservoir simulator (Powers).

Powers simulation capability has shown continuous and significant increases over the last decade. For example, Powers is capable of running over 17 million-cell simulation model for the Shaybah Field in a few hours — an accomplishment that was inconceivable a few years ago.

Saudi Aramco is currently developing the next-generation of Powers, called Powers-2, capable of running simulation models in excess of 100 million cells by 2008. Powers’ superior simulation technology allows Aramco to make more accurate predictions of filed performance and advanced diagnostics, enabling it to implement the most optimum field development.

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