1. business and industrial

Improved Cost Control with Digital Oil
Field Chemistry
John-Arne Stokkan, Schlumberger; Bengt Ellingsen, Schlumberger; Tor Sigve Taksdal,
Schlumberger, Hector Manuel Escalona Gonzalez, Schlumberger
Tekna Oil Field Chemistry Symposium 2015, 22-25 th March 2015, Geilo, Norway
Abstract
The traditional data management and reporting within oil field chemistry and flow assurance has been
driven by somewhat conservative thinking. Although digital solutions has been a topic over the years the
oil field chemistry domain remains conservative in this regard. Highly focused on reporting chemical
usage and dosage, analysis trends and financial parameters, and perhaps not so much digging in to the
large potential in understanding data and data dependencies. Making an analogy like going to a medical
check-up that does not only include blood pressure and coughing into a stethoscope; instead there are
multiple indicators that isolated may not tell the whole story, however, when combined we get a better
view on the health status and if intervention is required before there is a problem.
The oil field can in such perspective be viewed as a living organism with veins, pulse and organs. Each
dependent on each other, where one defect can impact the flow further down the line. Overlooking
indicators due to cost saving or lack of management strategy can prove to be costly as issues are often
not captured in time to take a proper decision. It is all about data quality, amount and frequency. A
single data point is rarely information, however trends and capture changes are essential. The cost of
investment in proper data acquisition, management and visualization will in most cases be small
compared to the value of saved cost from less intervention and extended facility lifetime. Knowledge is
the key and in this sense, experts are always scarce, so capturing best practices becomes as well
essential for continuous improvement.
We present a novel approach using software suites well known for production data management,
simulation, real-time visualization and dashboard information. By extending production data
management solution (PDMS) into production chemical data management (PCDMS) and put in use as a
hub for data management, simulation and surveillance. We have analyzed the requirements, available
features and possibilities for a digital oil field solution for the future with SCADA, real-time visualization,
performance intelligence dashboards (PI) and function as a decision support system (DSS) focusing on
the right information required to manage operational changes. This approach with automation reduces
need for costly interventions and keep the cost of operation under control, allowing for better decisions
in a combination of key performance indicators on an operational, tactical and strategic level.
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Introduction to digital data management and automated processes
Digital oil field, asset management and decision support
In the last decade, there has been an increasing focus on digital oilfield solutions. High speed
communications now link production facilities with centralized operations support Centers. Major oil
companies are investing strategically in their digital oilfield initiatives; “integrated operations”, “field of
the future”, “remote operations”, “smart fields”,” I-fields” and “field monitoring”.
Digital Oil Field in this sense, is regarded as the cross-domain combination of People, Processes,
Technology and Information, through the entire production system, in a way that allows the
optimization of the use of resources, increasing the efficiency of the overall system, with the aim to
minimize the repetitive tasks to be done implicitly by humans, in particular those that do not generate
value. By enabling this process in an integrated and automated digital oilfield solution or decision
support system, the value is then materialized (14).
The ability to provide performance metrics at the right time through the entire production system is
fundamental to be able to optimize cost and maximize the value of a producing field. The technology
available today gives the engineer the data required to monitor production and flow assurance
parameters, assuming that deviation from a performance parameter will be detected by monitoring the
received data, and that a good decision for improvement and optimization will be based on this data.
The base foundation for the Decision Support System architecture (1) is built with two fundamental
components:
1) A Data Integration Platform (Figure 1) that performs various data management aspects, such as:

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
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2
Automated Data capture, validation and sharing
Data Quality improvements using in build processing tools
Data Integrity and Traceability
With scalable data model and functionality
Offers broad range of reporting and graphing options
Figure 1. Data integration platform
2) A Production Surveillance and Engineering Desktop (Figure 2) that:

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Provides the required set of tools for data analysis and modeling
Allows the analysis process to be done in an efficient way
Feeds continuously from the data integration platform
Enables faster and more reliable decisions
Figure 2. Production Surveillance and Engineering Desktop
Production chemistry and flow assurance parameters and measurements
The art of production chemistry and flow assurance is a complex operation depending on many
parameters and thorough understanding of operations and production facilities. We know that changes
in operational and physical parameters have an impact on flow assurance and in many cases the effect is
not directly observed, but a related effect occurring further upstream or downstream from the point of
observation. Understanding operational and flow parameters is the key to understand and predict
causes and effects. This understanding is very much data driven and the ability to transform data into
information thus providing a foundation for decisions and actions.
Production chemistry and flow assurance goes a step further from production and operational
management as it is necessary to understand fluid properties and its subsequent changes as conditions
vary over time. This knowledge starts by understanding reservoir fluid properties and how it becomes
affected not only by change in PVT properties, but also impacted by mixing fluids, geochemical reactions
and the use of chemicals. A change in property that happens in the reservoir or when the fluid enters
the well may not become a problem until further change in properties make the conditions ideal for
causing flow restrictions or integrity issues.
Sampling, measurements and cost
The knowledge about fluid behavior and the ability to monitor changes is restricted by the amount and
quality of data. Brown fields may lack baselines for fluid composition, and also in Green fields the
amount of samples and type of samples (e.g. downhole pressurized samples) is a cost issue and is often
neglected in order to keep budget. Sampling involves cost as labor cost, equipment cost (especially for
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pressurized sampling), access to wells and analysis cost. The result can be questionable data quality to
be able to process data in order to provide information as decision support. The question is then; what
is the impact on decisions that are data driven when good quality data is not present? Does saving cost
in one end leads to increased cost in the other end when we cannot base decisions on reliable
information? Although it does not necessarily always lead to a severe impact as a scaled up well or
hydrate blockage etc., the risk certainly increases with unreliable or incomplete information.
Experiences have shown that there are different views across operators on the necessity to perform risk
analysis of the potential cost by minimizing data collection as a cost reduction measure.
Operational parameters
Fluids may contain a composition that puts it in risk for flow assurance issues such as scale, corrosion,
asphaltenes, wax, hydrates, napthenates, etc. However, it is when the operational parameters like
changes in PVT or facility design when the fluid may cause a problem. This could be in the near-well
area, well completion, production tubing, subsea line, riser, separation, water treatment, oil export etc.
We can verify dependencies between operational parameters and fluid composition; e.g. temperature
and wax crystals (wax appearance temperature and wax dissolution temperature) and thus indirectly
monitor the conditions for wax precipitation. Thus we can already see that operational parameters
impact fluid behavior and properties during flow and when combined with fluid parameters these
parameters are key performance indicators (KPI) that can be monitored individually or in scorecards.
Key performance indicators (KPI) and scorecards
In short, a key performance indicator is a measure for how well we are doing compared to a benchmark.
However, KPI’s are found in multiple levels, trending or status. KPI’s are defined from the question
‘What do we want to know?’ This implies that we need to know the question, thus we need to
understand our system and the chemistry and physics behind flow assurance problems than can arise in
a production facility. Even more the parameter and its value need to be recognized and acknowledged.
As already discussed there is a cost associated to sampling, analysis and monitoring. Do we need all
parameters? There is a saying:
“A value only has a value if its value is valued”
(Bryan Dyson, former CEO of Coca Cola)
So, what does it mean? It could mean that something does not have any impact and can be therefore
disregarded, or if the impact or meaning is not understood even a high risk KPI may be overlooked. Back
to the question ‘what do we want to know?’ We can coarsely divide our KPI’s into categories:
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Figure 3. A two-level KPI hierarchy, typical for a chemical supply contract. The performance KPI, parent, measured by each of the
child KPI’s. Each of the child KPI has a value and a score by its importance or desired contribution to the performance. The
parent is then a result KPI of its children in a scorecard.
Figure 3 shows a simple two-level scorecard where the parent KPI is a result of its children by their
individual score. The word Performance Intelligence (PI) is related to Business Intelligence (BI) which
gives a measure on how well we are doing. Business intelligence is used in financial data analysis,
although the real objective is to determine the performance, hence Performance Intelligence. KPI’s
stretches through a hierarchy of parent and child KPI’s rolled up into few but descriptive KPI’s. An
example of performance indicator contributors is shown in Figure 4.
Figure 4. Several contributors to a performance KPI with each carrying a value and a score. Each of the child KPI can have
multiple children and further.
The intelligence in Performance Intelligence mean that “hidden” indicators have a set of rules enabling
creation of smart dashboard visualization enables us to focus on the status instantly presented only
requiring action and further data analysis when alerts are showing. Figure 5 show an example of a
surveillance information dashboard.
As we already know; data is not Information. A transformation process is required to reach that label (12).
 Large amounts of data need to be treated carefully from the beginning.
 Automated and manual QC has to detect meter or gauge failures and typing errors.
 Merging and aggregation rules have to be defined appropriately and be transparent to the
engineer.
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The origin and traceability of the data (how it has been generated) has to be clear.
Data that derives from measured data has to be documented.
The data repository has to be organized and accessible.
The engineer has to identify the target, preprocess and transform the data, and evaluate the
results.
 Information can then be obtained and transformed into Knowledge.
To illustrate this: At the start of the day, the production engineer has two concerns: whether the wells
or production facilities are performing as expected and the reason in case they are not. But the actual
value of production is not enough by itself, it needs to be compared against a benchmark such as
planned or target value, to know how much the actual value deviates from it. The answer needs then to
be classified and ranked based on certain constraints.
The generation of proper Key Performance Indicators (KPIs) is also very important to accurately
represent the system in all its levels (reservoir, wells, facilities) and to provide insight into the possible
causes of deviation from specified targets.
The objectives (3) (9) behind surveillance are:
 Detection of opportunities. Integration of operational data and events, QC and QA
 Corrective alternatives. Analysis of problems and selection of possible corrective actions.
 Selection of candidates. Candidate ranking and economic analysis support.
As opportunities are executed, performance needs to be measured to assess the decision and provide
feedback into the surveillance process for continuous improvement and knowledge management.
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Figure 5. Sample Dashboard for Operational Surveillance for Meter Data Quality
Data management and automation
The digital oil field also includes automation of data collection where it’s possible. This is getting more
focus where investments can be justified by cost reduction, safety requirements or added value through
increased performance or sales. Using SCADA systems data can be captured and transported to
databases where data is organized, quality checked and stored. Linking other data analysis or simulation
software enables data analysis and scenarios to run on fresh data and even visualized with real-time
data.
In certain situations, in particular when there are more wells to look after in the production system, it is
not uncommon to spend ¾ parts of the time in a production engineer’s work day to perform routine
tasks of data cleansing, aggregation, calculation and visualization, leaving only a few hours for real
analysis and decision making. To add to the challenge, the process is not standardized and depends on
the individual preference or work practice.
To overcome these challenges, the decision support solution should be built in accordance with the
following targets:
 Maximizing available time for value added activities in the analysis life cycle, by minimizing the
time employed in data preparation and processing activities.
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 Provide the required range of functional capabilities to perform studies and analysis, from
knowledge driven, to data driven methodologies and streamline the process.
 Enable a big picture awareness that often is overlooked by working in siloes with little
collaboration or by often constraining the inputs and outputs from other domains.
 Enable Key Performance Indicators that enable comparison of planned vs actual performance,
and give information to improve current models as well as results interpretation.
To achieve better control over our asset and operation we need integration between databases and
applications. It has become more popular to talk about integrated software platform that communicate
across applications; as shown in Figure 6 as an example of software platform integration and
relationship between applications for data analysis, simulation and visualization. With a rapid changing
industry; applications need to be highly configurable to meet new requirements in a changing
environment.
Figure 6. With Digital Oil Fields and Asset Management data capture & historians, simulation models, data analysis, decision
workflows and visualization are all linked. Automating data collection is only a part of optimizing field surveillance, linking data
to simulation models allows real-time and performance data to be visualized with predictions and scenario models.
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Asset Data Management and Production Data Management Systems
(PDMS)
When implementing the Digital Oilfield Decision Support System, the Data Management tasks, Analysis
and Reporting has been noted to improve to a greater extent than other areas in the process execution,
such as Standardization, Integration and Automation. Typical improvement ranges from 30% to 90% in
the overall workflow (9).
The life of equipment, such as compressors, pumps, etc. can also be extended by monitoring their
operational parameters, and change operational settings to keep them running optimally, where all
required information is readily available in the data repository (11).
By making use of the readily available information, the analysis of Events and their consequences, for
example production shortfalls, becomes straightforward and can be visualized easily through Business
Intelligence tools (2):
Figure 7. Shortfall Analysis with Volume lost vs main causes (Pareto Chart)
One of the critical aspects of the implementation of field data management is a big increase in Reporting
Efficiency (~70%). The ease to create both pre-defined and user driven reports is a task that if done
manually takes a good amount of time from the engineer (1) (6).
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Figure 8. Pre-defined Reports can be generated automatically and send to specific recipients.
The provision of the official hydrocarbon volumes to the financial department, Partners and
Government enabled through the Production Allocation process (Figure 9) allows for a better
distribution of costs, understanding of the Asset and materializes more effectively the impact of
operational activities. When value has been tracked and shown financially, activities such as work overs
or remediation programs can be justified more easily (3).
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Figure 9. Allocation Network where fiscal measured production adjusts reference production at the facility and well level.
Other benefits include improved Data Ownership and responsibility, cross-discipline data sharing and
security.
As shown in Figure 10, the added value is identified as the added production resulting in the
improvement from the time a particular event is detected (T1), to the time when analysis takes place
(T2) and finally to the time the action is taken in the field. T1 can be reduced to a minimum with
automation and data quality improvements, while T2 can be employed more effectively by the focused
engineer. T3 is more dependent on the organization process and response.
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Figure 10. Added Value - From Event detection to Operational Action
Tying it all together into dashboards (Figure 11) demonstrates how information is visualized used in
different levels in the organization (operational, tactic and strategic) to help the Decision Making
process (3)(9).
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Figure 11. A visual portal with easy access from a web browser, directed at three different organizational levels.
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Value through Production chemical data management systems (PCDMS)
Adding value and optimizing cost
The objectives with setting up systems for data management and reporting:
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Getting control over your asset with information for making decisions
Reducing time from data collection to transformation into information enabling quick response
Cost control by optimizing chemical treatments and supply
Added value from cost control and optimizing treatments
When evaluating cost related to chemical usage and treatments, often it’s been measured in money
spent on chemicals. A common remedy has been to reduce the use of chemical. This is short-sighted and
most likely only postponing problems. However, this is usually a result of missing information, which
lead us to the question if we have enough data. We have established that data is not information, and
we can also say that incomplete, missing or low quality data also gives incomplete information leading
decisions to be in best case educated guesses. However, it’s not only incomplete data that may lead to
misleading information. The same is true when the value of the data or data dependencies are not
understood by those who process data into information. The operational and technical knowledge are
essential.
Let us look at some examples. We know the factors contributing to corrosion (13). These are child
indicators that have a value, target and a score that contribute to the parent, corrosion KPI. The
contribution (score) for each depends on availability, quality, frequency, reliability, impact and
sensitivity. Figure 12 shows an example on corrosion KPI by its contributing indicators.
Figure 12 Indicators / parameters that make a base for a corrosion KPI. Each child indicator has a value, target and score /
weight that tells something about how each impact the corrosion.
We can do the same for calcium carbonate scale as illustrated in Figure 13 with the factors contributing
to scale as indicators.
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Figure 13.Carbonate scale KPI illustration with child indicators that affect the final scale KPI. Each of the child indicator has a
value, target and score/weight.
Comparing the child indicators for both corrosion and carbonate KPI we observe common indicators.
The difference is how their behavior impact the result or parent KPI. In each case they will have different
target.
Thus when we are setting the score for each parameter/indicator we need to evaluate its impact to the
result (parent KPI). For example, production downtime or equipment failure is an indicator that
something is wrong and can be used in evaluating treatment performance e.g. by measuring reduction
of number of downtimes and failures over a time period. In a surveillance dashboard we would like to
see early warnings that enable us to act and change operational parameters or treatment strategy
before a more severe indicator as downtime or failure happens.
We will here provide some example scenarios for surveillance monitoring and performance intelligence
based on projects we are working on to demonstrate the information found in data and how to use
them to add value and control cost.
Production Chemical eManagement and automated data collection
When we talk about improved data collection we do need to talk about investments in hardware. The
benefit and profitability will however differ from field to field. A cost-benefit analysis is required caseby-case and should be a part of any field revisions under improvements. We would look at requirements
as how critical is the availability of data in a short time frame. This depends on how sensitive the
production is to changes. Some production facilities have few issues, and performance monitoring using
historical data is sufficient. Other fields have more critical issues requiring a constant surveillance to
avoid costly intervention from flow assurance or integrity problems.
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Let’s look at some examples. In case of corrosion surveillance we start by investigating the parameters /
indicators (13) that yield the corrosion KPI.
Indicator
Measured
Data collection
method
Possible collection
improvements
Iron, Fe
Chloride, Cl
Bicarbonate, HCO3
H2S
Water analysis
Water analysis
Water analysis
PVT analysis
Iron sensitive ion electrode
Chloride sensitive ion electrode
CO2
PVT analysis
pH
Water analysis
Sampling
Sampling
Sampling
Sampling
Field measure
Sampling
Field measure
Sampling or modelled
Corrosion rate,
modelling
SRB
Simulation models
Online biostuds, rapid in field
analysis. Future automatic field
measurements
Pressure
Operational data
Temperature
Operational data
Oil
Operational data
Water
Operational data
Water cut
Operational data
Field sample kits and
incubation
Side stream rigs with
biostuds
Pressure gauges
Simulation
Temperature gauges
Simulation
Flow meters
Allocation
Flow meters
Allocation
Calculation
Well downtime
from corrosion
Equipment failures
from corrosion
Corrosion rate
Operational data
Operational
Operational data
Operational
Corrosion coupon
Treatment
volumes
Treatment
Collecting corrosion
coupons for laboratory
measurements
Manual tank
measurements
Microbiology survey
Increase sampling frequency.
Online H2S measurement
Increase sampling frequency.
Online CO2 measurement
Online pH measurement in well
stream
Data availability through
database connections
Data availability through
database connections
Data availability through
database connections
Data availability through
database connections
Data availability through
database connections
Data availability through
database connections
Data availability through
database connections
Corrosion probes with Wifi or
mobile network for real-time
data capture
Automatic tank measurements
Flow meters with mobile data
collection
The table above shows the most common indicators that when combined yield a measure for corrosion.
Each of the parameters has a target model and score/weight. The target models are somewhat universal
as we know what indicators should be low and those who should be on target or as high as possible. The
score/weight is very much field dependent. The data quality as reliability and frequency is a factor when
setting the score. Some parameters are not even possible to measure.
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The following table also gives an example of common data collection methods. Let us review some
typical field equipment that improves data collection from a digital oil field perspective (15).
Measurement
Investments
Cost
Benefit
Ion electrodes with
data capturing
Probes,
data
collection
network,
SCADA
Software license
Flow meters, data
collection network,
SCADA
Software license
Remote operated
valves
Software license
Capex
Reduced Opex
Corrosion probes
with data capturing
Probes,
data
collection
network,
SCADA
Software license
Database direct
connection
Communication
network / open
firewall
Capex
Reduced Opex
(manpower)
Reduced cost of
intervention and /
or hardware failure
-
Quick access to data.
Rapidly capture changes to fluids.
Benefit systems where time is essential
setting KPI alerts
Accurate measurements.
Combined with dosage calculation and
alert KPI capture critical under/overdosing.
Enable quick reaction from surveillance
dashboard alerts on critical treatment
regimes.
Automated response by dosage
requirements reduce risk of wrong rate
Corrosion rates are regarded as a critical
parameters. Automating data collection
with real-time surveillance dashboard
enables quick response to deviation
avoiding costly intervention.
Flow meters on
chemical injection
with data
capturing*
Two-way
communication
dashboards*
Capex
Reduced Opex
Reduced cost of
intervention
Capex
Reduced cost of
intervention and /
or hardware failure
With existing historians the access to
data should not be underestimated.
Trends, prediction and real-time
calculation depends on access to data
stored in other resources. In other
words; go away Excel…**
Mobile
devices Mobile device
Capex
In fields where automated data
data capture
Software license
Reduced Opex
collection is not a solution due to cost
or other reasons, equipping field
engineers with mobile devices may be a
solution. With Wifi/GSM/3G/4G etc. it is
possible to capture data in the field, set
validations and upload to database.
Combined with e.g. barcode improves
data validation.
*By capturing flow data and reliable production data it is possible to set automatic adjustment of injection rates
based on dosage requirement and calculation of rate. Using dashboards with two-way communication the rate can
be operated from the dashboard or automatically based on pre-set criteria avoiding both under- and overdosing
chemicals.
**Microsoft Excel is a great tool for personal data analysis and calculations. However, it should not be used as
database or source for other systems because lack of security, validations and version control.
Experience has shown that when data management is also followed by improved workflow and
automated collection reduces time spent on collecting- and manage data as well as reporting.
Automated measurements also means quicker access to information for making decision impacting cost
and earning.
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Scenario 1 – Corrosion surveillance
In this scenario we analyze workflows for monitoring corrosion in wells in a land field being regularly
treated by shot trucks. The current situation:
 The treatments are managed in a database providing the drivers with a schedule each day on
wells to treat.
 The truck drivers record chemical treatments on papers and report back to supervisor.
 During treatment the truck drivers take water samples that are sent to the laboratory for
analysis.
 To save cost the operator only request iron analysis
 Corrosion is only measured by iron loss calculated as mass production from available water
production. No iron baseline available (old field)
 Production data is only available from well tests and measured on the headers only 3 days a
week.
 An access database was used for reporting with intricate queries leading up to 3 days to print
reports for about 700 wells.
An analysis of the workflows and reporting showed that savings could be made by
 Improving data collection
 Changing data collection and reporting software
 More corrosion indicators to improve the accuracy of corrosion surveillance
 Obtain better production data
By changing the access database to a platform with structured data in SQL and with dedicated standard
data captures screens and data loading capabilities the reporting time was reduced from 3 days to about
15 minutes for 5 print reports on 700 wells with complex calculations. Estimated 4-5% of the time spent
on reporting per month saved and available for other tasks.
The next step was to enable web based interactive dashboards for corrosion surveillance. The nature of
the current data frequency and quality did not require a real-time dashboard, so the dashboard is set to
refresh once a day to keep processing resources at a minimum. Figure 14 shows one of the dashboard
for corrosion surveillance where the corrosion KPI equals the iron loss. The dashboard enables both the
account team and operational team to access and check status at any time, and reduce time spent to
identify wells at risk with as much as 80% using KPI status to visualize any wells at risk, instead of flipping
through 700 paper copies.
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Figure 14. Example of corrosion dashboard combining status with trends. The KPI table on the right shows status for the wells in
a selected field. Any alert or warning is a result of the combined child indicators. Selected indicators for trending is placed in the
middle for the selected time period to capture any deviation linked to other events. The trends are filtered by the well list and
date filter. The presented dashboard shows average iron analysis (count) and iron loss (mass) with sum production and sum
chemical usage for a header (collection of wells). Individual wells can be investigated by using filter.
The next phases in improving corrosion surveillance for this field includes:
Action
Improvement
Time period
Estimated cost-benefit
Equip truck drivers with
mobile devises
Remove paper copy
schedule.
Electronically log treatments
with upload to database with
data validation
Short term
Extend analysis
Add pH measurements
More frequent H2S and CO2
measurements
Using frequent corrosion
modelling
Short term
Set automatic pull of
production and operational
data for use in calculations
and reporting
Short time
Remove printing cost
20% less time spent on
schedule
60% less time spent on QA
treatment data and transfer
from paper to electronic format
More accurate corrosion KPI
and information about the well
status
More accurate corrosion KPI
and information about the well
status.
Better understanding of hotspots in the wells.
With allocated and more
complete data gives more
accurate calculations and
information. Saving time used
in Excel up to 50% saved time.
More frequent modelling
Direct access to
operational database
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Short term
Barcode on wells
Access for laboratory
Flow meters on trucks
Truck and stock tank
automatic levels
Apply same principles for
topside treatments
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Ensuring truck drivers use
the correct treatment
program. Ensuring data entry
on correct well
Enable laboratory personnel
to enter results directly
Short-mid term
10-20% reduction in QC and
data validation for supervisors.
Short term
Enabling recording accurate
volume directly to mobile
device without manual data
entry
Enabling alarms and alerts
on tank levels. Initiate
product order based on level
measurement.
Accurate and real-time
volume usage per tank and
well.
Mid-long term
(Capex
investment)
Improved control and
management of all chemical
treatment in performance
and surveillance dashboards
Short-mid term
Reduce time for analysis
reporting to status update by
about 40%
Further reduce data
management time with up to
20%. Automate data QC. More
accurate volume reporting.
10% time reduction in level
monitoring and chemical
ordering. Eliminating shutdown from no available
chemicals. Accurate volume
usage for continuous injection
and dosage control to eliminate
under- or overdosing.
Reduction in time for data
management, reporting and
interpreting by 20-50%
Long term
(Capex
investment)
Scenario 2 – categorizing wells by scale risk
Scale is a critical problem for many wells and intervention can be high cost to remove deposits when
they have been allowed to form. In the worst case if scale has been allowed to slowly deposit, or we are
exceeding the amount of scale dissolver treatments a well can tolerate, the completion may need to be
pulled at high cost. Scale surveillance is therefore a low cost option to avoid high cost intervention.
In the following scenario the objective is to capture the scale indicators and categorize wells based on
their scale risk. We capture:





Ion analysis – Ba, Sr, Ca, Mg, bicarbonate, pH, suspended solids
Operational data – oil, water, water cut, CO2, HS2, temperature and pressure
Operational failures and downtime
Simulation data – pH, scale risk (scaling index, delta scaling index)
Treatment data – chemical dosage (calculated as injection rate x water * 10-6 ppm)
We set our criteria for the risk categories as well as target value and score for each indicator. Our scale
category KPI becomes then:
𝐾𝑃𝐼 𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 (𝑆𝑐𝑎𝑙𝑒) =
𝑇𝑎𝑟𝑔𝑒𝑡
∫
𝑇𝑎𝑟𝑔𝑒𝑡
{[𝐶𝑎2+ ], [𝐵𝑎 2+ ], [𝑆𝑟 2+ ], [𝑀𝑔2+ ], 𝑝𝐻𝑚𝑜𝑑𝑒𝑙 , [𝐻𝐶𝑂3− ]𝑡𝑢𝑛𝑒𝑑 , ∆𝑄𝑤𝑎𝑡𝑒𝑟 , ∆𝑄𝑜𝑖𝑙 , ∆𝑊𝐶, 𝑆𝐼, ∆𝑆𝐼, 𝑑𝑜𝑤𝑛𝑡𝑖𝑚𝑒, 𝐷𝑐ℎ𝑒𝑚𝑖𝑐𝑎𝑙 }
Where [𝑋 𝑛 ] is the concentration of species, Qwater is water production (loss in water), Qoil is oil production (loss in
oil production), WC is water cut (change), SI is scaling index and delta SI is change in scaling index and Dchemical is
the average chemical dosage.
Each of the child indicators have a score thus the KPI category (Scale) = ∑ 𝐼 × 𝑆 (where I is child indicator
and S is score).
The final scale KPI is the category defined by certain criteria for this field that classifies; red = high risk,
amber = moderate risk, green = low risk.
As this is not a surveillance dashboard we do not use the sensitivity as a score criteria but look at impact
and likeliness to happen as criteria for the score. Using historical data as analysis, production and
operational downtime combined with scale simulation models we can categorize wells into areas where
we need to focus. The model is updated at certain time intervals e.g. every 3rd, 6th or 12 months to verify
if the category landscape has changed. Using GPS coordinates for the wells we can visualize the
categories in a map as seen in Figure 15.
Categorizing wells like this enables focus on only those who require attention and may reduce treatment
requirements anything between 10% and 40% or even more. The major investment is data collection
and setting up criteria and dashboard. As the dashboard is connected to live data in Avocet PROCHEM
we only need to refresh the dashboard to obtain new data assuming frequent data collection.
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Figure 15. Example of scale tendency dashboards. The map shows wells based on GPS coordinates and color labeled based on
scale risk categories. The underlying scale indicators performs a check of indicator values against targets rolled up to a result
scale KPI identifying wells based on their risk categories. The map also function as a filter for the other dashboard elements
allowing data drill down. The top right shows is the first drill down with a well completion diagram where the markings are KPI
indicators for each location where there is a measurement or calculation. These KPI markings are both child and parent KPI’s i.e.
they contain a hierarchy under with more data. The bottom right is highlighting scale risk from simulation model in a PT
diagram where the size is the scaling index value.
The same visualization can be used for surveillance by changing categories to result KPI using real-time
data capture or over a shorter time period and include indicator sensitivity in the score. Data frequency
and quality then becomes more important, and need to initiate a cost-benefit analysis for improved data
collection. A surveillance dashboard will show wells requiring immediate attention and thus reducing
further risk of deferred production or downtime.
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Scenario 3 – Process diagram alert with details
In many cases we would like to monitor a specific system. With the visualization tools available we are
able to combine process diagrams with KPI status and alert. In this scenario we have picked a water
treatment facility process where corrosion is being closely watched.
The available parameters for monitoring are listed in Figure 16 and the corrosion KPI is a combination of
these with set target and score. An image of the process is added and hot spots created for each
monitoring point showing the KPI results. Each hot spot filters a spark line chart and a last result grid
table enabling data drill down.
Figure 16.Example of dashboards with monitoring points. Each monitoring point is a KPI consisting of child KPI that are weighed
based on impact, likeliness and sensitivity to yield a result KPI. When hovering over a KPI more information is provided in a text
box and filter the spark lines and last measured numerical values to the right. The spark lines give more details about the
parameters providing additional information to determine the cause of the alert.
Such visualizations hugely reduce time analyzing data as it pulls data directly from the database and
gives a status at any time. When the hot spots are green no action is required. Expanding the child
indicators can be done when one or more change color, to determine which one is missing its target and
set appropriate action. The risk is data quality and data frequency which need to be ensured by
sufficient data collection workflows.
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Scenario 4– identify top chemical spend wells
Within production chemistry there is a high focus on chemical spend and chemical usage. This focus is
motivated by cost, product (oil) quality and environmental concerns by water discharge.
The challenge lies within how to measure chemical usage and how to break it down into areas of
interest. We can measure chemical volumes by:
Measurement
Description and challenges
Chemical delivery
Gives total volume with chemical spend over a larger time period. Useful for chemical
usage and spend KPI field totals for quarterly and yearly review. Also useful comparison
against actual usage.
Tank volumes
Tank volumes measurement may be either manually measures (tank strapping) or using
flotation devise to measure levels. Manual measurement may not be accurate and the
frequency of data may be low. A chemical day tank often feed multiple injections (wells,
topside) and this feed may not be completely known thus only totals by product are
measured.
Injection rate
Some installations have flow meters measuring the flow for each recipient. When
chemical distribution cabinets are used for wells it creates additional uncertainty as the
distribution is mostly depending on well pressure. Using allocation models e.g. with
Avocet would enable tracking of chemical flow to recipients and through the process
and to discharge (16). Parameters as pressure and oil/water distribution can be used in
such calculations.
Let us also look at some chemical KPI’s. Looking at volumes or chemical spend may only contain part of
the story. The cost-effectiveness is performance vs. cost. But how do we measure performance? We can
track dosage, but it does not tell us how effective the chemical is. We need more to evaluate:
KPI
Description
Chemical volume
Consumed volume as total product or per treated system
Chemical volume per
production
Tracking how much volume required per system and per production provides
information about cost-effectiveness of the treatment
Chemical spend
Reporting on chemical spend provides a total cost for chemical uses that can be
broken down to treatments and treated system
Chemical spend per
production
Tracking spend per barrel of oil or water provides the cost-effectiveness for
chemicals in terms of production and lifetime.
Chemical spend per oil
price
This will show how much the chemical spend is of the product (oil) sale.
Chemical spend per
production and operation
The real cost-effectiveness is not only measured in production uptime, as this does
depend on other factors. Including more factors as increased equipment lifetime,
reduced intervention etc. will provide the real cost-effectiveness of a chemical.
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Tracking chemical spend depends on the availability of data. As shown above the data quality depends
on how it is measured and where. In some cases we can compensate for not having flow meters
available by using allocation. This is well known from production allocation. Avocet includes an
allocation module when used for chemical enables tracking of chemicals not only to recipients but also
throughout the process stream adding calculations with known parameters for chemical distribution.
Figure 17 is a dashboard example for simple volume usage by well which answers the question “what is
my highest consumer of chemicals?” We can use bar diagrams or more effective the decomposition map
where a big square equals highest consumer filtered by top 20 wells by usage.
Figure 17. Chemical usage and spend dashboard example. The left side show volume and spend for each well with color codes
for product. The middle shows a decomposition map for the top 20 wells by chemical volume – larger size means higher volume.
Under the decomposition map the production trend. The dashboard is filtered by date filter and shows data within the selected
time period. To the right some sums are displayed for the period selected.
More advanced dashboards can expand the decomposition map to show e.g.
𝑈𝑠𝑎𝑔𝑒 𝐾𝑃𝐼 =
𝑐ℎ𝑒𝑚𝑖𝑐𝑎𝑙 𝑠𝑝𝑒𝑛𝑑 (𝑢𝑠𝑑)
× 𝐼𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡 𝑓𝑎𝑐𝑡𝑜𝑟
𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑜𝑖𝑙 (𝑏𝑏𝑙)
Where the improvement factor contains uptime as a direct results of chemical usage, subtracted
downtime not related to chemicals and increased equipment lifetime.
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Scenario 5 – improving logistics
When measuring production chemicals its focus is often on chemical spend or performance in the field.
However part of chemical supply is additional cost for example related to chemical delivery and tank
management. Ensuring delivery on time is one factor and also optimizing tank management.
The next scenario looks at tank deliveries and circulation of transport tanks on permanent hire. The first
objective is to identify how much time each tank spend on a location and increase turnaround to reduce
the cost of hire. The second is to identify inactive tanks that could be taken off hire and rather be ad-hoc
hire tanks.
Figure 18 shows a timeline for each tank based on status and location. By setting target value (max time)
for the time spent on each different location we identify, by amber or red color, where we need to
initiate actions. The result is reducing unnecessary cost related to idle or stuck transport tanks.
Figure 18. Tank tracking using location and KPI status. The objective is to reduce time on certain locations improving turnaround
and thus cost. In this scenario tanks are on permanent hire; meaning taking idle tanks out of hire reduce hiring expenses.
The next question is about the data collection process. How do we know where the transport tanks are?
How often do we get the information? Usually we are in the mercy of staffs in different location to
record and send information, and not uncommonly there are certain weaknesses with such data
collection. Delayed data result in delayed information and unable actions to be made as the tank already
may have left the location although days overdue. There are different measures to improve data
collection. Barcode scanners is one solution, however routines must be established to ensure data is
immediately loaded to the database to become available in the dashboard.
RFid and GPS are other systems for tracking. With these linked to satellites or network with SCADA data
appear in the database by the frequency set for updating. There is a larger investment than e.g. barcode
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however when combining reduction in manpower and enabling quicker turnaround to lower hire cost
the investment may be worth the money.
Figure 19 illustrates a scenario with GPS tracking where the location is seen on a map as KPI spots
changing colors from counting days at each location combined with a hire cost overview.
Figure 19. Tank location and cost tracking example. By equipping the transport tanks with GPS their location can be visualized
on a map. The location points is also set up as KPI checking against time and cost criteria. When a tank should have arrived and
are overdue the location marking change colors. This example also shows a live cost tracking based on time out.
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Data Quality
Challenges in data collection processes
One of the biggest challenges in the data collection process in today’s environment can be explained
with the recent creation of the Big Data concept.
Big Data is categorized as having the three V’s: Volume, Velocity and Variety. With the technology
available today, the measured data from one well can easily reach more than 100,000 data points per
day. High frequency data needs to be processed in a similar speed as it is generated and the sources and
formats of the data are multiple and dispersed.
The amount of automatically generated data can overwhelm analysis. It requires aggregation “to report
one number” from the hundreds of thousand values that are collected in the field.
On the other hand, some information still needs to be recorded manually, this is where data collection
and transmission can result in errors, especially when transcribing to a spreadsheet or custom solution.
Excel is widely used and often as quick mechanism for collection, storage and computations. Companies
get swamped by an “Excel storm”, thousands of spreadsheets that make collating and rolling up
information difficult. It is also very difficult to track changes.
Figure 20. Swamped by an "Excel storm"...
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The impact of incomplete data
Often we come across incomplete data. We have seen data without dates, or even analysis with analysis
date rather than sample date. Anytime we capture data, either through reading or sampling we are
taking a snapshot of the system that is valid for the specific time of the snapshot. The production system
changes character over time due to changes in operational parameter or even slugging. Certain
indicators are therefore only valid within a time slot, and the larger time slot the larger the uncertainty.
As Figure 23 illustrates when we have gaps in our data it gives challenges for performing calculation. For
instance mass production from ion analysis and water production, chemical dosage from injection rate
and water or oil production. We can fill gaps by interpolating but do we know if this is correct? What if
there was no production. This affect calculations and totals. What is the total production over the time
period? What is the total chemical usage?
timeline days
Oil
Water
Injection rate
Analysis
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
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Figure 21. Illustration of a timeline with gaps. If we set the time interval to be days and we have gaps we may run into trouble
with calculations.
Data collection vs. cost
It is required to take into account the investment made on the infrastructure and measuring devices in
order to have the data available. In some cases human taking measurements are cost-effective or the
only possible approach when automatic data measurement and infrastructure is not available. But the
value of information is obtained after data is processed. Although more available data is desirable, the
aim is to balance the amount of measurements with the value of information the data generates, to
minimize cost.
It is important to understand the data and its value by performing cost-benefit analysis by application
and system when looking at possible improvements. Data management programs should include
analysis of each parameter including value for monitoring and the added value to understanding
processes and changes in processes.
Automatic data measurement vs. human measurement vs. cost
The “link each piece with each other” build mechanism often leads to what is called “organized chaos”,
where companies get comfortable for a period of time. Sooner or later they realize that it becomes
difficult to sustain these operations for a longer time, especially in changing or growing operational
environments or when subject to regulatory compliance and audits.
The proposed solution aims to reduce the complex “data gymnastics” and build simple, yet effective
mechanisms for E&P companies to collect, compute and distribute production information. The core is
the cost-benefit analysis that should be updated regularly as technology changes both with new
equipment and changes in cost. Not all parameters provide enough value to be valued to justify Capex
investment, and some critical parameters add enough value to justify investment. This differs from
system to system.
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Ensuring data quality and data organizing
Some of the methods to increase data quality can be embedded in the Data Management platform, in
order to trigger warnings or errors when a specific quality rule has been breached. Missing data can be
carried forward (repeating last available value), interpolated (if available as sporadic data) or calculated
based on additional parameters (e.g. through an equation or data-driven model).
Software application with custom made screens for data usually eases data management and reduce
risk or wrong data in the wrong place. Figure 22 shows an example of such screen and in combination
with data validation based on rules, as shown in Figure 24, the user will be notified when adding an
unexpected value.
Figure 22. Using dedicated screens with business logic for capturing data reduce risk of error in data when doing manual data
management. Using software platform with easy configurable screens means the ability to adapt data management to most
workflows and business scenarios.
Often we will need to connect to other data sources as databases, SCADA and even Excel. Automating
data collection or simplifying data import with rules and easy data mapping also reduces time spent on
data transformation and manual data management. Again coupled with data validation we have tools to
review and validate data before becoming available to data analysts and in dashboards and reports.
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Figure 23. The ability to write and use data loaders eases the data management process and combined with data validation
ensures correct data in the correct container.
Specific business rules are required to handle invalid data (e.g. through a numerical limit validation) as
shown in the following example:
Figure 24. Enhanced Data Quality through validation rules
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Discussion
The oil field industry transformation
The oil and gas industry is progressing in implementing cost effective solutions that prepare the stage
for more advanced workflows. With higher water production, increased cost of operation,
unconventional oil field and falling oil price the oil field industry will be even more cost-driven. More
effective solutions, smarter investments and optimizing operations will be the drive for towards the
future. Cost-benefit analysis will drive the investment in new technology pushing towards smarter
solutions.
There is a huge untouched potential in data analysis, mainly due to “tradition” where data was pieces of
information we collected so we at least knew something. Data management has entered a new
movement where Big Data, Analytics and Cloud Computing are playing an interesting role in the general
discussions. The action translates in the need of a company to ‘jump on the train’ before it leaves. With
more complex operations, higher focus on operational cost and need for making the right decisions we
need the tools for decision support to minimize “educated guesses”. The information we need is found
in data which require tools that improve data acquisition, data transformation and presentation to
support decisions.
When we take a look at oil field production chemistry there has been a progressive development the
last 30 years or so, with many different contract models:
 CALL OFF: Simple contract for product supply with limited service following the serious work of
winning the business. Limited management input e.g. monthly off take report. Having been
selected to supply a product we then wait for the orders to roll in! Of course it is never that
simple!
 MASTER SERVICE: As Call-Off but will generally cover several products and may include certain
specified services; offshore manpower rates, Tank rental, Pump hire etc. Generally involves a
higher level of input especially supporting field application.
 PREFERRED SUPPLIER: Evolved from Master Service Contracts with restricted number of
suppliers covering a single or group of assets.
 SOLE SUPPLIER: As Preferred Supplier but one Company manages total supply chain. Includes
contractual definition around field support. Contract personnel may be paid for. Will likely
include supply of competitive products. May cover supply of “a-typical” items such as; Utility
products, maintenance chemicals, sub-sea control fluids.
 CHEMICAL MANAGEMENT: A step further from sole supplier but with highly developed sole
supplier arrangement, detailed business relationship, contract team embedded within clients
facilities keeping high focus on Total Cost of Operation (TCO). This being achieved with high
degree of measured competence and performance targets and often based upon an open book
contract with incentive schemes with audit and peer reviews as key to long term, measured
success.
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 DIGITAL OIL FIELD AND ASSET MANAGEMENT: As we have moved into a new century there is
more focus on digitalization of the data management and reporting. Automation with data
management for decision support will drive the industry forward to a more knowledge based
workday. As new data collection equipment becomes smaller and less-expensive it will become
a motivator for automation of old field, including production chemistry and flow assurance. The
drive is cost, and reduction of cost by smaller investment in equipment and software reduces
need for manpower and reduces risk of failures or downtime with proper surveillance.
The production and asset management has taken steps into the future with digital oil field and
production chemistry and flow assurance is not far behind moving from traditional data management
into smarter solutions. As production chemistry and flow assurance is one of the most complex areas
only proper data management supported by chemical knowledge can simplify the complexity and
translate into information we can use.
Capex investments in automation. Does is pay off?
It has been proved that in general, the investment on automation pays off significantly fast. From one of
the references, the fact of a one-time visualization of the right information at the right time paid the
cost of the Decision Support System by 10 times.
The effort to systemize can be summarized with the following benefits:
 Efficiency improvement – The organized chaos has gone so far that it is becoming difficult to
operate.
 Cost reduction – maintaining either Excel or custom solutions is draining IT resources and a
better solution is required.
 Outgrowth – growing operations are exacerbating the organized chaos problem. This is a
derivative of the efficiency improvement problem.
 Technology turnover – changes in operating platforms, databases, operating systems, requires
that companies refresh current solutions.
By standardizing systems and data management we achieve:
 Global asset management – The effort to get all producing Assets into one platform,
allows companies to leverage the economies of scale.
 Regulatory compliance – Subject to growing country laws, listed and unlisted companies
need solutions that can be audited.
The foundation for optimization is to enable the move into other levels of integration, like Model Based
Surveillance or Integrated Operations, requiring data management aspect to be covered. Without this
foundation, companies are not able to take advantage of the newer technologies that would help to
increase production in an optimal way.
Impact on decisions from data management
Proper data management improves decision making, by reducing time spent in non-value added tasks,
increasing data quality and integrating across domains in the organization. Decisions can be taken with
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better support and justification, and increased available time will avoid making premature decisions
based on poor data.
Using visualizations and dashboards for monitoring and surveillance of assets and KPI comparisons the
information becomes more available and increase understanding of what is happening within the asset.
The capability to monitor assets and compare their main indicators, for example, utilization ratios, loss
efficiencies, fuel efficiencies, treatment performance across assets and how these translate into bottom
line benefits. Comparisons allow ranking of assets to occur and a focus on targeted actions can result
from such analysis. Using KPIs, actual production trends, companies have a better visibility on how their
production may occur.
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