SPE-167879-MS

"Smart Decision Making Needs Automated Analysis" Making Sense Out Of Big Data In Real-Time Brian Crockett/Wipro; Kshitiz Kurrey/Wipro Copyright 2014, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Intelligent Energy Conference and Exhibition held in Utrecht, The Netherlands, 1–3 April 2014. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

Problem Statement

Due to the availability of large quantities of real-time data, many operators are faced with the challenge of extracting meaningful information from it. In many cases, analysis of the data, the updating of models for Wells, Reservoirs and Facilities requires human intervention. When WRFM teams do not have analysed information, there are delays in the decision making to process required for optimisation.

Objectives

To demonstrate how auto analysis can reduce time consuming and repetitive tasks of comparing real time data with operating envelopes. Exception based applications and the updating of multiple operating envelopes and models without human intervention, enables the analyst to concentrate on anomalies and opportunities to optimise. The constant visualisation of analysed information should assist the decision making process.

Method

• Highlight the problems of managing large quantities of data • Show how alerts can be sorted, analysed and prioritised automatically • Explain how operating envelopes and models can be auto-updated • Visualise analysed information using digital signage

Results • The initial deployment of the EBS system produced more than expected anomalies • Human activity is concentrated on higher value-add actions • Daily reporting was streamlined • Visualisation was increased

Conclusions

The analysing of data by automated methods means that all wells and facilities are under surveillance and updated models are available for production system optimisation. Analysts have more information and time to focus on critical issues rather than mundane tasks.  

Applications This approach can be used for wells, reservoirs and facilities surveillance and model updates.

Innovations 1. Monitor by exception 2. Auto updating models 3. Better visualisation 4. Increased productivity

SPE-167879-MS 2

Significance of Subject Matter

This demonstrates how very large amounts of data can be analysed and translated into meaningful information for managers to take key decisions.

SPE-167879-MS 3

Introduction

Due to the availability of large quantities of real-time data, many operators are faced with the challenge of extracting meaningful information from it. In many cases, analysis of the data, the updating of models for Wells, Reservoirs and Facilities requires human intervention. Because of the time it takes to carry out routine analysis, WRFM teams are experiencing delays in the decision making process required to optimise their wells and facilities.

This paper seeks to look at ways of automatically processing routine data in a number of different ways to satisfy the needs of:

• Well Production and Performance • Facilities Optimisation • Well Optimisation Opportunity Generation • Operations Visualisation

Drowning in data and thirsting for knowledge

In the first SPE Intelligent Energy Conference in 2006 we were all talking about retro fitting instruments to our wells and facilities so that we could receive data in real time instead of waiting for an operator to physically go out and take a reading. Now we are faced with very large amounts of data coming in from thousands of instruments in some cases every fraction of a second. Data Historians like PI have given us a repository from which to mine and analyse specific tranches of results and translate them into meaningful information. For limited amounts of data this could be handled using Process Books and training the operators how to use them, but as the amount of data increased, this practice became unwieldy and we had to resort to Exception Based Surveillance and we will discuss this later in relation to wells and facilities.

The interaction of different applications using source data for processing that data and passing it on for further analysis has brought about increased integration and the need for data quality and assurance. This is not discussed here but should be taken into consideration.

Presenting raw data is essentially boring and difficult to understand and therefor there is the need to take that data and visually present it in different ways to suit different audiences. We want to see what is happening.

The deliverable that Intelligent Energy should give us is the ability to turn data into information so that we can make the right decisions.

Shell Smart Fields produced the value loop philosophy to execute the process of taking real-time data all the way to decision making as follows:

Assets Physical assets are described as equipment with instruments and sensors constantly sending out data in real-time. These can be wells, pumps, vessels, compressors, etc.

Data Thousands of sets of data are produced every minute and goes unnoticed unless there is an alarm or a trip. Distributed control systems in Central Control rooms generally only have the capacity to store data for no more than 5 days before it is overwritten. Data historians acquire the data and store it for analysis.

Analysis and Modelling Figure 3 The Value loop It has been said that “Data is useless unless you do something with it,” and there is a need for analysis and modelling. This is where a great deal of collaboration takes place as differing disciplines, skills and experience can be combined to resolve issues and provide solutions to the problems facing the operators in the field.

Plans and Decisions Decision making and action taking closes the value loop and should be a lean process adding real value to the asset. The mantra of Smart Thinking is:

Ø Know Sooner Use real-time data

Ø Decide Faster Engage in collaborative working

Ø Perform Better Support increased production of between 6 – 10% and contribute to reduced deferment.

SPE-167879-MS 4

Exception based Surveillance Many assets have oilfields with more than a thousand producing wells to monitor, which means that the Well Analysts or Production Programmers find that they can only concentrate on the high producers. This results in low producers performing sub-optimally and in some cases shutdown. A significant production loss happens because these wells are not monitored continuously. Exception based surveillance uses operating envelopes to monitor wells and facilities in real time. An operating envelope is a set of parameters within which the well could be described as operating at its normal producing level. If any of these parameters are exceeded then the well would be deemed as malfunctioning or performing abnormally. An Exception Based Monitoring System (EBS) compares the real-time data with the upper and lower limits of the Operating Envelope and where necessary will highlight any exceptions as an “Alert.” With EBS the Production Programmers and the Production Technologists can address the deviation faster as the information related to the alert is more readily available.

How Alerts are sorted An EBS has the flexibility to use its metadata and group information under different headings like:

• Cluster • Field • Reservoir • Responsible Production Programmer/Production Technologist • Well type e.g. Water Injection, Self-Producing; Beam Pump, PCP, ESP, Gas Lift etc. • Kind of alert e.g. Low flow rate, High Annulus Temperature etc.

How Alerts are prioritised The alerts are prioritised in 2 ways

• Kind of alert - Depending upon the criticality of alert, they will be labelled Red or Amber. For e.g. High Tubing Head pressure will be a Red alert, as it can affect the well integrity.

• Production levels - Depending upon the levels of production of the wells.

An automated dual sorting method works best for the prioritising the alerts, where the alerts are first sorted on the basis of the kind of alert and then displayed in descending production level within each kind of alert.

Taking Action

These alerts will be acknowledged by the respective alarm owner and they will suggest the solution to it. An automated workflow using Standard Operating Procedures is used, so that the alert is tracked to its resolution. Once the alert is resolved it will automatically disappear from the list.

Facilities health surveillance The same principles which have been outlined above for wells can also be applied to other facilities like vessels, pumps, compressors and tanks etc. Equipment health surveillance can be carried out, potential risks identified and corrected before a trip takes place. EBS gives the Surveillance Engineer like; Process, Rotating, Static or Reliability the ability to be proactive in:

• Identifying the issue

• Providing a solution

• Resolving the problem

In the past Engineers were required to examine raw data in the form of trend analysis and rely on their experience to determine if things were OK. Often this was looking for peaks and troughs. This high level approach only works if there is a radical problem but fails to identify e.g. when equipment is operating at the wrong level. A steady horizontal line does not necessarily mean that everything is working as it should.

The EBS can harness the qualities of the data historian by detecting a rate of change in an instrument. It can predict that at the current rate of change a piece of equipment will drift out of its operating envelop and potentially trip or shutdown.

As in Monitor Well Production and Performance, the pre-processed information in the EBS helps the Engineer to focus on the critical pieces of equipment while still having vision of all the other pieces of equipment which need settings corrected to bring them back into their optimum working condition.

How pre-processing helps EBS The simple process of matching an operating envelope with PI data is not difficult. Providing the Tags are correctly matched then it works. No one can say, “The EBS is wrong.” It can’t be wrong, either the Operating Envelop is wrong and it needs to be changed or the instruments sending the readings to PI are malfunctioning and need to be fixed.

SPE-167879-MS 5

The advantages experienced so far are that; wells which were not producing or underperforming have been identified sooner, the number of wells being maintaining at the optimum level has increased, and the number of equipment trips has been reduced.

It must be emphasised that the new way of working using the EBS is the main driver for success.

SPE-167879-MS 6

Auto-updating

Results from well tests are critical for hydrocarbon allocation and keeping well models evergreen. Also operating envelopes for wells and equipment need to be reviewed and updated regularly so that exception based surveillance can take place. The process of analyzing, screening, verifying and validating often requires a number people reviewing the same information before the updating takes place. The aim of auto-updating is to screen out the routine normal results where no change is required and present the exceptions for further analysis.

Well test validation Well testing varies across the industry but a “Lean” process adopted by one major oil operator seeks to automate the process as follows:

• Auto schedule well testing • Auto start test • Manually monitor during the test • Auto compare the results with previous tests • If the results are similar to the previous tests then manually approve and

transfer to a Hydrocarbon Allocation tool like Energy Components • If the results differ from the previous results then run the test again • If the second result is still different, then it is transfer to Energy Components for further analysis unapproved

and the analyst is alerted.

This means that system is not fully automatic but reduces the number of people involved in routine tests and permits normal tests to be auto approved.

Well model updating Well optimisation teams will use well models to test out potential opportunities for maximizing the production potential of the well. The model therefore needs to be as true a reflection of how the well is currently performing. In order to keep the model evergreen, the Production Technologist/Well Analyst will compare it with the latest well test results and any other production data from the well itself. Well models, for example, in “Prosper” are opened up in the application and then the Production Data like Tubing Head Pressure, Casing Head Pressure, Temperature, Flow Rate etc. are fed into the application to compare them with the model. In most cases there is an interface with the data historian so that no manual intervention is required. Well Test results including laboratory sampling are normally stored in a Hydrocarbon Allocation tool and would need to be manually uploaded to the modelling tool. When all the information has been collated in the modelling tool, a comparison needs to be made to see if the model requires updating or recalibration. In the same way that the well tests were auto approved, the well models can be automatically updated. Provided that the changes are with strictly controlled parameters there should be no need for the analyst to carry out this task. An exception based facility will alert the analyst when a model is significantly deviant from normal. This is particularly advantageous when a very large number of wells are being tested and where the analyst is responsible for hundreds of wells. To automate this routing, boring and repetitive work, frees up the analyst to carry out more value added tasks.

Daily Reporting Every Morning, Production Platforms and Production Stations compile a Daily Report containing the last 24 hours statistics. This report would typically contain:

• Oil Target and the actual amount produced • Produced Water • Water Injection volumes and pressures • Gas Balance • Flaring • Scheduled and Unscheduled Deferment

This information is available in a number of different places and can be automatically gathered together in one place for validation and distribution. We need to move away from spreadsheets using complicated macros to pull the information in for validation. In one Operating Unit they have used their Real-Time Optimisation Portal to gather and rationalise the information. The Report is stored in a location which is security protected and where only restricted personnel have access. At 08:00 a.m. the Report is approved and frozen and made available for viewing. This does away with the need for the spreadsheet to be attached to an email and sent to a large mailing list. There are many ways this information can be sliced and diced for different audiences.

SPE-167879-MS 7

Visualization In the Digital Oilfield, visualization is everything. Collaborative Work Environments and Integrated Operations Centres are awash with audio-visual tools. The only problem is what to pull up on the high definition screens?

InfoChannels

The way forward is to present real-time data as a “News Channel” which is asset specific and constantly updated. In many National Oil Corporations, there are Asset Teams, Field Teams, and Cluster Teams etc. They want to see material relating to their operation. Visualising tailor-made information which is circulating on the screens gives users the key knowledge they require without having to constantly look it up. This can be achieved by using an application which acts as an auto-bot to search out information from a variety of data sources and present them on a screen in a specific format.

Each “News Page” needs to be specifically designed to position the gathered information in the desired layout. In one NOC we were able to give each of the nine asset teams their own identity but still remain within a corporate format standard.

Data Sharing in Optimisation Teams The InfoChannel screens should be positioned next to optimisation teams. These teams need to sit together to optimise wells or facilities. In a typical CWE a seating plan, a four desk arrangement can be used to provide a personal working space for each member of the team, the ability to communicate face to face and create a conference table between them as shown. The “InfoChannel Screens” permits information on individual PC’s to be shared on a large monitor with the whole team.

Geographic views The positioning of alerts and data in a geographic way allows the coordinators and supervisors to make strategic decisions regarding deployment. Each well has a physical location with GPS coordinates which can easily be plotted on Google Earth. If you display key data next to the well and link it to an Exception Based Surveillance system and flash the data if an alert occurs, you can then plot a campaign of action for the Field Operator. The same principle can apply to the facilities in a platform or production station. This geographical visualisation gives greater clarity when a single fault makes an impact on a number of wells or facilities in the same location.

Results • The initial deployment of the EBS system produced more than expected anomalies

Initially when the program ran it produced thousands of anomalies. Some were instruments wrongly tagged in PI, others were where operating envelopes had not be updated or calibrated since first production and yet others were the result of communication and bad data issues which had never been detected. In the beginning progress was slow until the basics were fixed.

The EBS resulted in a specifically targeted method of resolving well issues rather than an ad-hoc or firefighting approach.

More wells were brought back into full production in a faster time, typically 50% faster.

• Human activity is concentrated on higher value-add actions Auto-updating released analysts to conduct more optimisations.

The Production Programmers who verified the Well Tests were released to spend more time monitoring well production and performance resulting in more wells operating within their operating envelopes.

Production Technologists were able to use their well models without having to go through the lengthy process of checking and updating them. This meant that more well reviews could be conducted and a resultant rise in optimisation opportunities was achieved.

• Daily reporting was streamlined The Daily Report was auto formatted in different ways to suite different audiences and email traffic was reduced.

SPE-167879-MS 8

• Visualisation was increased Elements of the Daily Report were broadcast to the whole asset team.

Ancillary information like weather reports, flight times and key items in the Integrated Activity Plan were automatically updated instead of populated into a PowerPoint presentation manually.

The information being displayed in the asset on the InfoChannel screens was a source of pride and gave a sense of a very productive unit.

Geographical display permitted a more strategic approach to operations.

Optimisation teams were able to share information more readily.

Conclusions

The analysing of data by automated methods means that all wells and facilities are now under surveillance. Even small increases in production replicated over a number of wells can result in a significant variation. In a climate where every drop of oil counts, Exception Based Surveillance is essential.

Auto-updating of well models releases the analysts to focus their efforts on critical issues rather than mundane tasks.  

Visualisation has a dual effect of presenting asset material as well as giving the team a sense of pride.

Pre-processing means that very large amounts of data can be analysed and translated into meaningful information for managers to take key decisions.

Together we can make it happen.