SPE-213674-MS
Power of 6 Sigma to Overcome Chronic Business Pain Area
Adil Albusafi, Farah Albalush, Mohammed Alshuily, Najim Albalushi, Maadh Alrahali, Ashraf Alhinai, Ali Alsahlani,
Houd Alsalhi, Yousuf Alkiumi, Nayakam Ram, Jithin Muhammed, and Somaya Maghdari, Petroleum Development
Oman
Copyright 2023, Society of Petroleum Engineers DOI 10.2118/213674-MS
This paper was prepared for presentation at the Middle East Oil, Gas and Geosciences Show held in Manama, Bahrain, 19 – 21 February 2023. The official proceedings
were published online on 7 March 2023.
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
Production reporting management in oil field has been one of business pain area, with a cumbersome manual
allocation process, causing argument over the estimation of produced volumes. Main challenges identified
were the inaccuracy of involved meters, multiple production reporting tools, reactive mode decision making,
staff exposure during BS&W sampling and Tank dipping. Hence, tremendous integrated efforts using Six
Sigma Green Belt Lean approach were made to simplify and sharpen the production reporting process, as
well as minimizing the human factor in it.
This project undergone Six Sigma Green Belt Lean project approach as below:
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Define stage: define project status, scope in/out, desired status, anticipated business benefits, …etc
Measure stage: developed detailed "As Is" process pap identifying all critical X's and data
collection plan
Analysis stage: developed cause & effect and data graphical analysis (Scattered Plot, Matrix Plot,
Box Plot, Dot Plot, Correlation, Regression)
Improve stage: generate ideas for potential solutions & prioritization, failure mode & effects
analysis show reduction of RPN and Pilot was run where X was optimized accordingly
Control stage: developed control plan, standard Operating Procedure (SOP), Visual Management
Board (VMB), Leader Standard Work (LSW) & To Be process map and reported business benefits
Replicate stage: developed replication candidate alignment & plan
These efforts have resulted in a pronounced business benefit such as:
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1.2% Oil Gain
5.7% deferment reduction
Full Time Equivalent (FTE) saving of 2.69, which was wasted on various parts of the old
methodology
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Process enhancement of production reporting and making data available which increased the
confidence in production data
Better focus on low-performing fields to identify surface and subsurface opportunities &
bottlenecks
Quick, simple & effective solution for operational standard procedure that improved system
availability, production gain & team proactiveness
Eliminating several high-risk activities by automation several process
Early identification of leaks through new developed tools
Flare Reduction by early identification of LCV failure and improving measurement system
(automated)10% flaring reduction in field-x as result of fixing LCV failure
Build the 1st PDO Live Production Management & Monitoring tool which is replicated in other
four assets in Oil North Directorate with ongoing replication in the Oil South Directorate.
This innovative 1st PDO Live Production tool is a powerful utilization of the 6 Sigma deep data analysis
to insure safe operations of the business processes through:
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Optimize operational control systems to eliminate personal & process safety exposure
Enhancement of system stability & reduction of trips and human interventions
Operating Assets within approved Operating Envelope (OE)
Background
Hydrocarbon production allocation and reporting in some areas, faced several challenges, with manual
production reporting process, causing lots of confusion and argument over the allocation of produced
volumes and initial reporting from site. The daily production reporting fluctuations increased despite earlier
efforts to simplify and sharpen the reporting process. Over the past two years, numerous of tools been used
for daily Production monitoring, and it helped to solve some issues but not discrepancies in reporting and
missed sustainability part.
On an average, it takes more than 4 hrs in some cases to identify the production drop that leads to
unscheduled deferment for the day. There was discrepancy between daily estimated production and actual
allocated, around than 35% on an average on daily basis. Early identification of discrepancy will help
the team to prevent production loss. Below figure-1 showing the discrepancy of production data between
estimation and actual.
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Figure 1—Production Discrepancy trends
Methodology
This project undergone Six Sigma Green Belt Lean project approach.
Generally, Six Sigma is a problem-solving methodology that helps enhance business and organizational
operations. It can also be defined as:
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A quality level of defects per million opportunities
A rate of improvement of 70 % or better
A problem-solving methodology of DMAIC (Define-Measure-Analyze-Improve-Control) using
data-driven and statistics
An initiative taken on by organization to create bottom-line breakthrough change
Sigma is a Greek symbol represented by "σ". A term "Sigma" is used in statistics to represent standard
deviation from mean value, an indicator of the degree of variation in a set of process. Sigma measures how
far a given process deviates from perfection. Higher sigma capability, better performance. For a process,
the sigma capability (Z-Value) is metric that indicates how well that process is performing.
Six Sigma is a systematic problem-solving method that is placed around defects elimination and variation
reduction which leads to improve overall process performance.
The key principle tool in Six Sigma is the use of the DMAIC methodology. DMAIC is a logical framework
that make the team to think over and plan for enhancements/improvement to a process in aim of achieving
higher Six Sigma level of excellence.
DMAIC (Define-Measure-Analyze-Improve-Control) Roadmap
There are five phases that are used in the DMAIC method as below:
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Define
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Project Selection
Project Charter
Project Plans
Measure
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Process Mapping, Y's and X's
Data Collection
Establish Metrics
Process Capabilities
Improve
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Optimization
Reduce Cycle Time
Decrease Variability
Reduce Costs
Reduce errors
Validation of Improvements
Measurement System Analysis
Failure Mode and Effects Analysis, FMEA
Analyze
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Graphical Tools
Statistical Analysis
Model Y = f(X's)
Designed Experiments
Diagnostics
Implementation
Control
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Develop Control Plans
Share Best Practices
Monitor Inputs and Outputs, Y's and X's
Ensure Gains are Sustained
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Figure 2—Six Sigma Phases
Use of Six Sigma as main process:
Oil North Operation team decided to use Six Sigma Process methods to implement in production reporting/
calculation variations in QA facilities as a pilot study.
6 sigma team been formed under the leadership of Six sigma lean practitioner with higher management
support.
Both South and North operation teams decided to improve team response to be proactive mode which
will reduce the impact in term of (deferment, manpower allocation) and to Reduce the discrepancy and on
time availability of data to sustain daily production as desired status
The Linkage to the Business was clear as below:
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Production and Deferment KPI in business scorecard is tracked in daily, monthly, and annually
There is a potential to replicate this initiative acrossThe main Goal Statement of this project were to:
Reduce the discrepancy of data from 35 % to 5%
Increase proactive response to sustain production By September 2021 and
Readily accessible Data
DEFINE Phase:
The Define phase is the first phase of Six Sigma process and it is important to Six Sigma's overall process
success. In this phase, Define is all about describing the problem and setting the project goals. We started the
improvement project by developing the project charter, understanding customer requirements, translating
those requirements into project requirements, establishing a change management plan, and working in a
team environment.
The main objective of the Define Phase was to refine the Project Team's understanding of the problem
to be addressed which was around production reporting high variation.
Step-1
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Define the Problem.
Voice of the Customer is very crucial to set the scene and expectations. They are two key customers of this
project where they require specific Oil production reporting accuracy to get maximize production benefits
with less hidden deferment. However, Customers not getting the desired accuracy in reported production
rate and high discrepancy been reported in provided data
Specific Need of customers were to Improve team response to be proactive mode which will reduce the
impacts in term of (deferment, manpower allocation) and to Reduce the discrepancy and on time availability
of data to sustain daily production
Baseline: using basic calculation method to check Sigma level & defects per million opportunities
(DPMO) the results as below:
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DPMO, Sigma level
Project Risks & mitigation at early stage:
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The main risk for this project is the delay in IT scope if required
Limitation in going to field due to Covid-19 & stop going to the field with minimum resources
RACI table been developed with main tasks and responbilites as shown below in Table 1.
Table 1—RACI table
PLAN:
Good project plan been created and well communicated with all parties as shown below in Figure-3.
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Figure 3—Project plan
MEASURE Phase:
Measure is the second phase of DMAIC. The core activity in this phase is to define the baseline which will
be used for next phases and data analysis.
Step-2,
In this phase, the first step in this phase is to measure the overall process problems and test for any
measurement variations. Before starting the data collection process, there is a need to decide the minimum
samples size required to be collected. Sample size is crucial for analyzing/improving the process. Using
Mintab software, samples size been estimated and below results (334) showing required minimum sample
to data analysis (Figure-4)
Figure 4—(title of the figure)
As is map been developed to show current process flow and to identify the critical X's.
Process Stability used to:
– To assess how is the process stable
– Measure the level of control in this process
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Below figure-5 showing the analyzed data for testing process stability.
Figure 5—process stability using Minitab software
Main findings from process stability test:
– Few points showing process instability which been checked and found due to operational issues
Station trips and upset
Next step was to test process normality and below figure-6 showing the results using same software.
Normality test showing the data was not following normal distribution.
Figure 6—(title of the figure)
Main findings from process normality test:
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The data are not normal as P<0.05,
– Most of the points fall on straight line
– More than 95% in the line
– All outliers been checked due to assignable causes (operation upset/trip)
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The third test in this phase was Process Capability Cpk: The OVERALL process capability index
combines the effect of variation with how well the process is centered, (Figure-7)
Figure 7—(title of the figure)
The main outcomes from MSA (measure system analysis) step:
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Three main pain areas been identified that having measurements variations.
Several actions been created to improve MSA.
Second attempt in Gage R&R after improving MSA, clearly showing improvement in Gage R&R.
New template been created to standardized production estimation/measurement
All the sources of estimations measurement variations been removed by this template
For improvement., currently there is no source of variations in measurements between operators,
therefore Gage R&R improved
This generates consistent values across all operators
ANALYSE Phase:
Step-3
In previous phase, tens of data been collected during measure phase; these data will be analyzed in this phase
for further investigation. The collected data will be used to identify the root causes using cause effects,5
whys and other 6 sigma techniques. Using Minitab software to analysis collected data, below matrix plots
been created to show if there are any relations between all the X's.
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The main outcomes from this analysis, several root causes been identified with clear plan to attend
them based on pareto chart. From analyze phase outcome, 2 critical controllable Xs been identified: Actual
Unstable Flow production of on stream (Root Cause: having more flushed gas i.e.is higher compared to
simulation data and Measurement of high GOR fluids coming from other stream (Root Cause: Several
streams without proper measurements of all of them).
Figure 8—Matrix Plot of all X's
IMPROVE Phase:
Step-4
The main purpose of this step is to develop and Implement proposed Solutions which been identified in early
step by Root cause analysis tools. Small Scale pilot was conducted in QA CCR using new excel template
to simulate all the coming streams. Using pressure sensor in Tiber fields to predict any production drop and
live production performance. Simulate the numbers of main streams coming to main station to predict the
overall expected production for the cluster.
Full Scale pilot was conducted on all streams in QA using new application (software-based application).
The application using artificial intelligence techniques to calculate current production and to predict future
production.
Using control chart, Comparison Discrepancy (Y) been done to show the data before and after Pilot
(Fig- 9). Clear indication of variation reduction after implanting the pilot. Below Boxplot confirming the
reduction in discrepancy (Fig-10).
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Figure 9—MR Chart of Data Discrepancy before and after Pilot
Figure 10—Measure phase analysis outcomes
Using same data to recalculate DPMO (before and after pilot), below are the results:
Discrepancy Improvements:
Before
After
DPMO = 748339
DPMO= 4941
Mean = 250
Mean = 26
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CONTROL Phase:
Step-5
The last most important step of 6 sigma DMAIC is to Sustain the Gain of all the previous steps. The key
activity in this phase is to control the improved process and ensuring the new process is implementing and
sustain. The following table showing control plan which been implemented to ensure that the results are
sustain.
Table 2—Control Plan
Automated VMB been created to track daily performance and LSW leader standard work prepared with
all the requirements as shown below in Figure-12.
Figure 11—Visual Management Board VMB
Conclusions
Utilization of the 6 Sigma deep data analysis in oil and gas production process to insure safe operations of
the business processes through:
SPE-213674-MS
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Improve operational control systems to eliminate personal & process safety exposure
Enhancement of system stability & reduction of trips and human interventions
Operating Assets within approved Operating Envelope (OE)
The main benefits of adopting Six Sigma in terms of cost saving and cost avoidance been realized
in deferment reduction and oil gain.
Overall Process enhancement of production reporting and making data available which increased
the confidence in production data
Improved concentration on low-performing fields to recognize surface and subsurface
opportunities & bottlenecks
Created the 1st PDO Live Production Management & Monitoring tool which is replicated in other
four assets in Oil North Directorate with ongoing replication in the Oil South Directorate.
Fast, simple & effective solution for operational standard procedure that improved system
availability, production gain & team proactiveness
Eliminating several High-risk activities by automation several process
Early identification of Leaks through new developed tools
Flare Reduction by early identification of LCV failure and improving measurement system
(automated)10K m3/d flaring reduction in field-x as result of fixing LCV failure