Special reportTransforming Subsurface InterpretationFinding Petroleum forum in London - Apr 13 2015

SAS - data mining on subsurface data

Seabed Geo Solutions - why seabed seismic recording gives better images

Dynamic Graphics - using fibre optic welldata with reservoir models

Geodirk - an automated system for usinggeology with seismic processing

Official publication of Finding Petroleum

Special event report - April 2015

Data mining in the oil andgas industry is about work-ing out which dependentand independent data rela-tionships are identified andmapped to your businessobjectives, said KeithHoldaway, advisory indus-try consultant with SAS In-stitute, speaking at the Apr13 Finding Petroleum Lon-don forum “TransformingSubsurface Interpretation”.

It usually involves both data scientists anddomain (oil and gas) experts, he said.

Mr Holdaway worked as a geophysicist atShell for 15 years, and subsequently has beenworking on ways to develop ‘machine learn-ing’ systems to try to complement traditionalways of looking at oil and gas data.

He is also the author of a book, “Harness Oiland Gas Big Data with Analytics: OptimizeExploration and Production with Data DrivenModels”, published in May 2014 by Wiley.

Data mining techniques can be used to try towork out ways to make models with the data,and generating hypotheses worth modeling,

to help you achieve your results.

“We try to integrate some of these analyticalworkflows and see if the data tells you astory,” he said.

All of the work needs to be geared aroundworking out an ‘objective function’, the rela-tionship between data elements which willhelp you achieve the objective you are look-ing for (usually, more oil).

A common trap is that people get very ex-cited with the relationships they discover, butthe relationships don’t actually help achievethe business objective, he said.

“Putting these [models] together from a datascientist perspective is one thing, but youmust be able to operationalise these modelsin an existing architecture. If you can't oper-ationalise you can't really gain,” he said.

Deterministic and probabilistic

Subsurface oil and gas data has many vari-ables, and works best with probabilities, tohelp people make the best decisions, he said. You need to work out which variables will

help you work out the probabilities in a highdimension input space.

The oil and gas industry has traditionallyworked in a very deterministic way, lookingfor the right answers, using ‘first principles’based on traditional scientific calculationsand equations, he said.

Oil companies have got to a situation wherethey are only using 20 per cent of the datathey have, because they only use data whichenable these equations.

Keith Holdaway – how to do oil and gas data mining

This special edition of Digital Energy Jornal is a report from the Finding Petroleum forum in London on Apr 13 2015, Transforming Subsurface Interpretation.

Data mining in the oil and gas industry is about trying to work out which hidden trends and relationshipsin the data coupled with appropriate data-driven models will lead you to the right answer, with datascientists working together with domain experts, said Keith Holdaway of SAS

Transforming Subsurface Interpretation

In this example, the computer has found a way ofgrouping wells according to their characteristics. If thegrouping makes sense to a reservoir or production en-gineer, it might point to where other successful wellscould be found, if they have the same characteristicsas a group of successful wells.

3Digital Energy Journal - Special report, Apr 13 2015 Finding Petroleum forum "Transforming Subsurface Interpretation"

Event websitewww.findingpetroleum.com/60143.aspx

Report written by Karl Jeffery, editor of Digital Energy Journaljeffery@d-e-j.com Tel 44 208 150 5292

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Transforming Subsurface InterpretationFinding Petroleum's forum in London on April 13, 2015, "Transforming Subsurface Interpretation", looked at ways that subsurface interpretationcould be improved by using state of the art technology, tradition geophysics / geology skills, and structured work processes.The opening talk was from Keith Holdaway, advisory industry consultant with SAS Institute, and author of a book, “Harness Oil and Gas BigData with Analytics: Optimize Exploration and Production with Data Driven Models”, published in May 2014 by Wiley.Mr Holdaway was followed by a talk on seabed seismic recording with John Moses, Sales Director with Seabed Geosolutions.A session on data integration included a talk on gravity gradiometry recording and data integration with Claire Husband, Senior Geophysicistwith ARKeX, and John Brennan, Analytics and Data Management Strategy Lead, Oil & Gas, Hewlett-Packard.A session on data visualisation and analysis included talks from Mike Leach, workstation technologist with Lenovo, on using high performancecomputing; Ken Armitage, managing director of Geodirk, on integrating traditional geological skills with seismic data processing; and JaneWheelwright, technical application specialist with Dynamic Graphics, on integrating fibre optic data from wells with other subsurface data.This special Digital Energy Journal report includes an outline of all of the talks. For most of the speakers, the videos and slides are availablefree of charge online - you can see how to access from the links at the end of the article.Karl Jeffery, editor, Digital Energy Journal

Explaining how todo data mining in oil and gas: KeithHoldaway, advisoryindustry consultantwith SAS Institute

Transforming Subsurface Interpretation

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But there is a big move towards probabilisticapproaches, which most ‘data mining’ tech-niques include, he said.

The Society of Petroleum Engineers recentlycalculated that the number of papers with theterm ‘data mining’ in it had increased by 20xin the past 7-8 years, he said.

The data modelling approach can be usedalongside the more traditional calculations,so you are using traditional and non-tradi-tional ways of looking at seismic data to-gether.

These techniques can be applied to any E+Pdata set, he said.

Reservoir characterisation

A typical oil and gas business objective couldbe reservoir characterisation, using subsur-face data to try to determine a higher fidelitygeologic model.

Geophysicists calculate “seismic attributes”,a range of different data properties calculatedfrom the seismic, such as amplitudes, coher-ence and variance.

It would be useful if you could map seismicattributes to reservoir properties, but to do thisyou need to know which attributes are themost useful from a pre- and post- stack per-spective.

Automated data mining sensitivity techniquescan help you work out which attributes mightbe reflecting something useful, and so whichones are worth studying more closely.

As an example, you can get the data outputin the form of ‘self-organising maps’, whichcan help you spot patterns and relationshipswhich might show you direct hydrocarbon in-dicators.

To map seismic attributes to reservoir prop-erties, you need both data scientists and reser-voir experts.

“You can't just go off and talk to PhDs in sta-tistics, they come up with these algorithmswhich aren't totally applicable to your busi-ness,” he said.

“We need some kind of visualisation alongthe roadmap, so experienced people can say,yes you're going in the right direction here.We may need to tweak something to improvethat process,” he said.

Trying to find the right answer is an iterativeprocess, not a process where you can enteryour data into a ‘black box’ and get the an-swer out.

In one example, Mr Holdaway gave subsur-face data for a field in Oman, which con-tained a known oilfield, to someone with datamining expertise in the medical sector, but nogeophysics expertise, and without telling himwhere the oil was. Deep Learning workflowswere implemented to identify features indica-tive of DHIs based on a recursive set of neu-ral networks.

“I said, do your thing as you did in the otherindustry,” Mr Holdaway said.

“He identified other areas close to a produc-ing well. It worked very well.”

Understanding production

In another example, you might have one wellwhich delivers good oil production, and awell nearby which doesn’t deliver anything,and you can’t understand why.

You can apply data mining techniques to tryto identify the variables which are consistentin both regions, and then use those as a basisto understand the second well.

You could use this to come up with a betterfracturing strategy for the second well, per-haps with a different proppant, based on whatyou have identified as the more importantgeomechanics of the field.

In one big oil company example, the com-pany had an employee who was one of theworld’s biggest experts on reservoir waterlevel. He claimed that he knew everythingthere was to know about water level in thecompany’s reservoirs, and no computer sys-tem would be able to say why water cuts insome wells are higher than others.

The SAS data mining techniques showed thathe might be wrong in how he was interpretingthe fracture network.

“His ego wasn't so big that he entertained theidea,” Mr Holdaway said.

“He decided to change the location of an in-jector well and producer well and it obviatedsome of the problems.”

“The data told him a story and he tried to giveit some credence, and managed to increaseproduction in that asset.”

The systems have also been used on produc-tion data, by one oil company which nearlydestroyed part of its reservoir with waterflood.

The data mining techniques came up with aprobabilistic system which could forecastproduction for a particular well with 90 percent confidence, he said.

This helped the company work out whichstages of the well to leave open and which toclose.

“All those kind of answers came through in-tegrated production data, PLT data, and someof the geologic, petrophysical data,” he said.

Expertise

A company in Oklahoma City in the US hasput together an ‘analytical centre of excel-lence’, where it uses many different softwaretechniques to try to help upstream engineers,he said.

The company hired data scientists who havesome understanding of the oil and gas indus-try.

“You have to be able to steer these guys sothey are not trying to create algorithms, tryingto be Newton and Einstein, and come up withsomething incredible which isn't useful to thebusiness,” he said.

Sometimes you need a lot of expertise to de-velop the right methods, but once they havebeen developed, you have a repetitivemethodology which does not need so muchskill to use.

“There's a lot of interaction between data sci-entists and geoscientists,” he said.

“The output from predictive models is fedback into the traditional tools. It will give youa much more robust visualisation.”

Managing data

There are three different systems in upstreamoil and gas – the reservoir, wellbore and fa-cilities. All of these generate many differentsorts of data.

The data has many different forms, includingstructured, unstructured, “big” data, high ve-locity data, real time data, batch data, spatial(related to space) and temporal (related totime).

Compared to other industries, the volumes ofoil and gas data are not particularly large, but the variety of data can be very large, hesaid.

Digital Energy Journal - Special report, Apr 13 2015 Finding Petroleum forum "Transforming Subsurface Interpretation"

The oil and gas has so much different data, there aremore possible relationships and correlations than ahuman being could figure out.

Transforming Subsurface Interpretation

Seabed Geosolutions – acquiring seismic on the seabedThere is growing interest in recording seismic data with nodes on the seabed, particularly with areas hard toaccess with streamer vessels, said John Moses of Seabed Geosolutions

There is a growing interest in recording seismicdata with “nodes”, or small recording devices,placed on the seabed, as an alternative to tradi-tional streamers towed behind vessels, said JohnMoses, Sales Director with Seabed Geosolutions.

Seabed seismic can offer a much clearer picture,and give you more flexibility in how you doyour survey, than towed streamer, he said, speak-ing at the Finding Petroleum London conferenceon April 13, “Transforming Subsurface Interpre-tation”.

The cost per km2 of seabed acquisition is still rel-atively high compared to towed streamer meth-ods for anything but reservoir scale studies, butthe added value of information combined withevolving operational methods has been enoughto persuade many oil companies to take theplunge to the ocean bottom.

The company is a joint venture between Fugroand CGG.

In the current oil price environment, spendingmoney on good data can be a cost cutting measure, Mr Moses said, if it enables drilling tohit the reservoirs more efficiently. “It is aboutde-risking the [drilling] decisions,” he said.Good decisions have to be based on the best in-formation.

There are many areas of the world which havebeen surveyed many times by towed streamer,such as the North Sea. Using seabed seismicrecording could illuminate the fields in a newway, he said, which helps understanding of thereservoirs and optimises the way they can be ex-ploited.

Seabed seismic recording is not a new technol-ogy, but in the past has mainly only been usedfor areas which could not be accessed with con-ventional streamers, Mr Moses said. This wasbecause seabed seismic used to be relatively ex-pensive and had technical limitations. The latestadvances have removed the barriers that previ-ously existed and are also starting to close thecost gap. At the same time the uplift in the valueof information is now being recognised.

The quality of seismic data improves withseabed recording. The receivers are able torecord both P and S waves thanks to their 4 com-ponent sensors. The sources and receivers arecompletely decoupled which gives the oil com-pany complete freedom to design full offset andfull azimuth data sets, recorded in a quiet envi-ronment with full frequency bandwidth. Re-ceivers can be placed in obstructed and shallowareas which are inaccessible by conventionalmeans. Both imaging and reservoir attributes aredramatically enhanced and offshore field infra-structure is no longer a barrier to data acquisi-tion

“We are seeing a quantum step change in qualityof seismic data, using receivers on the seabed,”he said.

The Dan Field showing the reservoir and wells - there are 58 oil producers and 50 water injectors.

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“Data management is one of the key issues,”he said. “We have to aggregate the data, in-tegrate in an analytical data warehouse.”

Data mining techniques

There are two basic data mining techniques,supervised and unsupervised.

With “supervised” data mining, you split thevariables into explanatory (or independent)variables and dependent variables. The aimis to try to find a relationship between inde-pendent and dependent variables. With “unsupervised” data mining, all vari-

ables are treated in the same way (there is nodistinction between explanatory and depend-ent variables) and you are trying to spot pat-terns and trends that cluster into characteristicprofiles.

The data mining methods will look at manydifferent models which could be used to giveyou the answer you are looking for.

Examples of data mining techniques includefuzzy logic, cluster analysis and neural net-works. “These are all approaches to let thedata do the talking,” he said.

“We have to quantify the uncertainty in thosevariables and evaluate the value inherent inthe patterns,” he said.

The process looks for trends and hidden pat-terns.

Principal component analysis is a methodol-ogy to reduce the high ‘dimension’ of thedata, if you can figure out which componentsare worth paying most attention to.

Watch Mr Holdaway’s talk on video atwww.findingpetroleum.com/video/1336.aspx

Digital Energy Journal - Special report, Apr 13 2015 Finding Petroleum forum "Transforming Subsurface Interpretation"

Helping you get a clearer subsurface picture by usingseabed data acquisition - John Moses, sales director,Seabed Geosolutions