Copyright 2003, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., 5 � 8 October 2003. 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, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes 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 where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Abstract This paper summarizes experience and results improving business performance using Lean Six Sigma and Lean in upstream oil and gas operations in North American and Asia. Background and history on Lean, Six Sigma, and ISO systems are provided. The role ISO quality systems can play in supporting Lean and Six Sigma is explained and demonstrated with examples. Overviews of completed Lean Six Sigma projects are provided for well testing, rod pump repair, water treatment, oil treatment, well stimulation, and production logging. Introduction The majority of oil and gas producers, as well as oilfield service and equipment providers have used or been exposed to quality and statistical concepts through various initiatives in their respective organizations. A partial list of programs many oilfield companies have utilized include:

• Statistical Process Control (SPC)1 • Total Quality Management (TQM) or Total Quality

Control(TQC) as defined by Ishikawa 2 • Baldridge�s Seven Quality Criteria 3 • Deming�s 14 Point System 4 • Juran�s Quality Trilogy 5

While these programs were often believed to be beneficial, few programs were sustained within an organization over multiple years. The specific benefits of these quality and statistical initiatives have typically not been well understood, quantified, or documented in the oilfield. Continuous improvement requires a sustained effort. This has been a significant challenge to the petroleum industry. Employees and management are often skeptical of the benefits and long term

commitment to these programs, because they have seen several of these quality systems come and go, without clear lasting impact. Six Sigma has evolved out of the work of Deming, Ishikawa, Juran, and others. Six Sigma is enhanced over past quality initiatives through a:

• Strategic alignment directed by leadership • A structured project execution process • A structured organizational deployment • A strong emphasis on business results and

financial verification of benefits Lean, focused on optimizing the customer value chain, was developed in the automotive sector but has transitioned into a wide variety industries. The most recent development in the last few years combines Lean and Six Sigma to create Lean Six Sigma. Continuous improvement is also facilitated in a structured framework such as ISO 9001: 20006 or API Q17. These frameworks can be used for controlling operating procedures, assessing the capability of quality systems, sustaining continuous improvement, and managing records and documents. These concepts, proven in other industries, can be adapted to the oil industry while building on the existing petroleum industry knowledge of quality and statisitics. Case histories are provided for organizational deployments using Lean Six Sigma, and ISO 9001 concepts to create sustainable continuous improvement in upstream operations of a large petroleum producer. Six Sigma Six Sigma is a process improvement methodology that focuses on delivering products at lower cost with improved quality and reduced cycle time. It can be summarized as method for reducing process variation. Six Sigma was developed in high-tech manufacturing in the 1980�s. In the last ten years, this methodology has spread to many industries, including aerospace, pharmaceuticals, heavy manufacturing, and transactional-service industries.8-11 Application of Six Sigma in the oilfield has been limited and has occurred only in the last couple years.

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Application of Lean Six Sigma in Oilfield Operations R.S.Buell* and S.P. Turnipseed, ChevronTexaco, *SPE Member

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Six Sigma can be defined as a:10, 11 • A measure of process capability (Cpk) • A set of tools • A disciplined methodology • A vision • A philosophy • A strategy

Defined mathematically, a Six Sigma process will have a process capability potential of 2.0 as given in equation (1), a process capability of 1.5 as defined in equation (2), and a defect rate of 3.4 per million opportunities or sigma level of six as defined in equation (3). Cp = (USL-LSL)/6σ (1) Cpk = minimum of [(USL-xbar)/3σ or (xbar-LSL)/3σ] (2) σlevel = minimum of [(USL-xbar)/ σ or (xbar-LSL)/σ] (3) A Six Sigma process, as can be seen in Figure 1, has six standard deviations between the process mean and the specification limits for a centered process.

Figure 1

Six Sigma Process Capability Defintion

-7 -6 -5 -4 -3 -2 -1 -0 1 2 3 4 5 6 7Standard Deviation

Low

er S

peci

ficat

ion

Lim

it

Upper Specification Lim

it

6 σ6 σ

Table 1 Overall Yield as a Function of Sigma Level & Process Steps

Reducing Process Variation with 6σ

No. of Parts or Process Steps +/- 3 +/- 4 +/- 5 +/- 6

1 93.32% 99.379% 99.9767% 99.99966%10 50.1% 94.0% 99.767% 99.997%

100 0.1% 53.6% 97.70% 99.966%1,000 0.2% 79.2% 99.7%

10,000 9.7% 96.7%100,000 71.2%

1,000,000 3.3%

Sigma Level

Red

ucin

g Pr

oces

s St

eps

with

Lea

n

A defect rate of 3.4 per million for a normal distribution corresponds to σlevel of 4.5. Motorola, in their original definition of Six Sigma, assumed that a process could shift or drift 1.5σ without detection.12 Thus, there is 1.5σ drift margin built into the standard definition of Six

Sigma. The relationship between σlevel and overall yield can be seen in Table 1 for a single step process. Overall yield is equal to one less the defect rate. As mentioned earlier, Six Sigma can be defined as a set of tools. Many of these tools are familiar from past quality and statistics initiatives. A partial listing of the more common Six Sigma tools include:

• Process Mapping � Process Flow • Cause and Effect Diagrams • SIPOC � Supplier Input-Process Output Customer

Diagrams • Pareto Charts • Histograms � Distribution Analysis • SPC � Statistical Process Control • Regression Analysis � Scatter Charts • ANOVA � Analysis of Variance • Hypothesis Testing • RCFA � Root Cause Failure Analysis • FMEA - Failure Mode and Effect Analysis • MSA - Measurement System Analysis • DOE � Design of Experiments • Lean Tools

As a disciplined methodology, Six Sigma projects typically use the DMAIC project execution process, which stands for the five steps of Define-Measure-Analyze-Improve-Control.8-10 An alternate but similar four step model, PCOR is also used which is Prioritize-Characterize-Optimize-Realize.11 Companies such as General Electric, Allied Signal, and Polaroid have embraced Six Sigma as strategy, philosophy, and vision. Business results and career advancement are strongly linked to the use of Six Sigma within these companies and many others. 8-9

A specialized branch of Six Sigma is Design For Six Sigma (DFSS) which is used to create a new process for products or services that are aligned with customer requirements and deliver Six Sigma performance from the outset. So Six Sigma can be thought of as a disciplined methodology for improving an existing process and DFSS is a disciplined methodology for designing a new product or service.13 Lean Lean has its origins in the automotive industry. It is also known by the terms Lean Enterprise, Lean Manufacturing, and Lean Production. The Japanese auto industry began creating Lean in the 1950�s on the foundation created by Henry Ford and Alfred Sloan.14 Lean is a process improvement methodology that focuses on removing non-value added activity and aligning production with customer requirements.14-16

Simply, Lean streamlines and optimizes process efficiency. Lean has successfully spread to other manufacturing and service industries but has seen limited application in the oilfield.

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Lean focuses on identifying and eliminating the seven hidden wastes common to both manufacturing and service industries, which are:

• Motion • Over processing • Transportation • Over production • Waiting • Defects and rework • Inventory

Lean tools used commonly to eliminate these seven wastes include:

• Value Stream Analysis • Error-Proofing or Pokey-Yoke • Takt Time or Pull Scheduling based on customer

demand • Kaizen-blitz • Visual Control • Five-S (Sort, Set in Order, Shine, Standardize,

Sustain) • Standardized Work • Kanbans - Just in Time Delivery • One Piece Flow • Quick Changeover • Total Productive Maintenance • Overall Equipment Effectiveness (OEE) • Six Sigma

OEE is commonly used to measure of process leanness and is defined in equation (4). OEE = Availability * Efficiency * Quality (4)

Availability = Run Time/Net Operating Time Efficiency = Actual Output/Target Output Quality = Good Output/Actual Output

This OEE model can be applied in the oilfield. An example is given in Table 2 for a rod pumped oil well.

Table 2

Example OEE Model for a Rod Pumped Oil Well

PercentageOperating Hours

per Month Hours per MonthAvailability 98.5% 733 744

Actual BOPM Target BOPMEfficiency 98.2% 600 611

Actual BOPM Less Rejected Actual BOPM

Quality 100.0% 600 600OEE 96.7% When the concept of OEE is used, the value chain, life cycle costs or total cost of ownership is optimized for a process. Lean does not optimize singular aspect of a process but improves the overall process value delivery.

ISO 9001:2000 ISO or the International Organization for Standardization has a Quality Management System as defined in ISO 9001:2000. The adoption of ISO 9001:2000 and API

Q1 is already well established in the petroleum industry service and supply sector as documented in the API Composite List.17 Additional references in the paper will be made to the ISO 9001 systems only. It should be understood that ISO 9001:2000 and API Q1 are very similar. However, ISO 9001:2000 and API Q1 are not identical. API Q1 is fully compliant with ISO 9001: 1994 but not with ISO 9001:2000. 18 API registers companies to both quality standards, and many manufacturers carry dual registration. 17 The ISO 9001 quality system management system provides a foundation to apply Lean and Six Sigma principles. Oilfield processes commonly have σlevels between zero and 1.5. Occasionally, oilfield processes will have negative σlevels, that is the process mean is outside the specification limits. See Table 3 which is based on the case histories and projects referenced in this work.

Table 3

Oilfield Process σlevel % Defects*Cyclic Steam Profit Negative -Water Cut - Shipment Plant 1 -0.4 65Acid Stimulation Profit 0 50Subsurface Steam Measurement 0.3 40Steam Distribution 0.4 66Oil Meter Allocation Factor 0.7 47Water Cut - Shipment Plant 2 0.9 19Well Testing 1.5 8Raw Water Discharge Quality 1.5 8* - % Defects varies due to 1 or 2 sided spec and process centering

Typical Oilfield Process σlevel Prior to Improvement

Processes with low σlevel values will have a large standard deviation indicating large variations or noise within the process and a lack of standard operating procedures. It should be understood that Lean Six Sigma projects have made significant improvements in some of the σlevels above. The process based design of ISO 9001:2000 enables continuous improvement and reduction of process variation through:

• Record and document management • Control of operating procedures • Elimination of process non-conformance through

corrective and preventive actions • A structured approach to assess process

effectiveness Combining Lean, Six Sigma and ISO 9001:2000 Historically the Lean continuous improvement advocates recognized Six Sigma as a tool that supported Lean. The Six Sigma advocates recognized Lean as a tool for reducing cycle time and inventories, but the two approaches were also viewed as competing with each other. In the last few years, the synergies between the

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two methods have been recognized and they have been merged into a single process improvement methodology � Lean Six Sigma.19-20 The most recent developments are referred to as Fusion Management, which combines Lean Six Sigma with ISO systems and the work of Baldridge, Deming, Juran, and others. Fusion Management can be thought of as a unified quality approach that bring together multiple systems. As can be seen in Table 1, there are two ways to improve quality in a process, by 1) increasing the σlevel or 2) by reducing the number of process steps. Lean Six Sigma facilitates process improvements by working both dimensions of Table 1, with Lean primarily focused on removing steps and Six Sigma focused on increasing the σlevel.

The examples that are provided later in the paper are the result of using Lean Six Sigma with the selected application of ISO systems.

Lean Six Sigma Deployment Design Considering that previous quality systems have not been sustained in most organizations, it is important to understand why this has occurred. Quality initiatives that do not yield clear bottom-line benefits and cannot be strategically aligned with business objectives will be the first thing cut out in a low oil and gas price environment. Keys factors in designing a deployment to deliver results and achieve strategic business objectives are given in Figure 2. 21-23

Figure 2

Some pitfalls to specifically avoid include:

• Starting in the middle of the organization or forcing it up from the bottom

• Discount the importance of linking Lean Six Sigma or Fusion Management objectives to business strategy

• Training without accountability for producing

results • Skipping the time consuming data collection phase A North American oilfield example plan covering key deployment factors is given in Table 4. The North American case histories later in the paper were managed within this deployment schedule. Verifiable financial benefits began to accrue at a approximately one year from initiation of the deployment.

Table 4 Example Lean Six Sigma Deployment

Deployment Process StepTiming - Weeks

from StartStart 1Evaluate Quality System Options 1-4Select Preferred Quality System 5-8Leadership Alignment and Commitment 1-10Select Consultant or Support System 10-14Communicate System to Workforce 14Train Executives - 1 day 15Train Champions - 3 days 15Select Projects 16-18Develop and Sign Project Contracts with Blackbelts 18Train Blackbelts - 4 weeks over 4 months 19, 23, 27, 31Train Project Team Members - 1 to 5 days 25 - 31Review Project Progress 23, 27, 31, 36, 42Finalize & Document Projects 42 - 52Financially Verify Project Benefits 71, 97Lookback at Overall System Benefits - Strategic Adjustments 53, 72, 98Communicate and Advertise Benefits 54, 73, 99Assign Blackbelts to Next Project 43 - 53Additional Waves of Blackbelt Training 50 - 66 Typical roles, terminology, and training used in Lean Six Sigma are defined in Table 5.

Table 5 Lean Six Sigma Role Definitions

Term RoleTypical Training

ExecutiveSponsorship - Build the Culture, Share

the Vision, Strategy Alignment 1 to 2 daysChampion or

SponsorManage Organizational Deployment -

Identify Projects & Black Belts 3 to 4 days

Master Blackbelt or Instructor

Have managed and completed multiple complex projects - Demonstrated

Competence with all Tools - Capable of instructing courses 5-6 weeks

Black Belt or Expert

Have managed and complete two or more complex projects - skilled with most

tools including DOE 3-4 weeks

Green Belt or Specialist

Have managed and completed at least one less complex project - proficient with

common tools 2 weeks

Yellow Belt or BasicProject Team Member - familiar with

common tools 1 weekWhite Belt or Introduction

Team Member - introduced to the process and tools 1 day

A typical Lean Six Sigma project is completed in 4 to 8 months, with project complexity and scope driving project duration. All projects follow the DMAIC project

Lean Six Sigma Deployment Input-Process-Output Diagram

Lean Six Sigma

Deployment

Projects Completed (%)

Cycle Time (months)

ROI ($MM/year)

Blackbelt Selection

Project Selection

Accountability Contracts

Training

Motivation / Reward

Project Tracking

Executive Ownership

Recognition & Project Scaleup (copied)

Champion Involvement

Customer Satisfaction

Inputs OutputsPROCESS

Lean Six Sigma

Deployment

Projects Completed (%)

Cycle Time (months)

ROI ($MM/year)

Blackbelt Selection

Project Selection

Accountability Contracts

Training

Motivation / Reward

Project Tracking

Executive Ownership

Recognition & Project Scaleup (copied)

Champion Involvement

Customer Satisfaction

Inputs OutputsPROCESS

84434 5

execution process. Table 6 maps DMAIC to typical tools used in Lean Six Sigma or Fusion Management. DOE (Design of Experiments) is one of the most powerful tools in the Lean Six Sigma toolbox. It can be used to optimize non-linear or complex interaction process responses.24-25 DOE has been successfully applied in the oilfield to complex chemical treating, thermal, and reservoir simulation problems.

Table 6 Typical Lean Six Sigma Tools Used

Define

Project ContractsQuality Function Deployment (QFD)IPO (Input Process Output) DiagramsPareto AnalysisFMEA (Failure Mode & Effect Analysis)

RCFA (Root Cause Failure Analysis)QFR (Quality Fitness Review) audit findings ISO 9001 & ISO 14001 audits findings

Measure

Process flow mappingCE (Cause & Effect) MSA (Measurement System Analysis)Graphical Analysis, e.g. histogram, run chart, etc.

Capability StudiesFive STakt TimeVisual ControlsBenchmarking

Analyze

Identify CNX (Constant Noise Experimental) inputs in CESOP�s (Standard Operating Procedures)Capability StudiesGraphical AnalysisStatistical Analysis - Hypothesis test, Regression

DOE (Design of Experiments) Screening Design ModelsDA (Decision Analysis)Simulation and probabilistic modelsEconomic AnalysisReliability Modeling

Improve

Control ChartsRobust DesignsLine BalancingHypothesis TestingDOEKaizan Blitz Quick Changeover

KanbanLayout DesignMistake Proofing (PokaYoke)Simulation ModelsGenetic AlgorithimsArtificial Intelligence & Automated SolutionsISO corrective & preventive action

ControlControl chartsControl PlansFMEAEquipment Reliability Plans

Mistake ProofingSOP�sISO internal audits

The black belts go through 3 to 4 weeks of training. They are responsible for managing the project and leading the project team. There is a 3 to 5 week break between each week of training during which they work on projects with their team. Because black belts are critical to the success of a project and ultimately a Lean Six Sigma deployment, it important to understand desired black belt attributes. Some of those attributes include: • Strong facilitative leadership skills • Respect within the organization from peers,

supervisors, and subordinates • Results focused • Excellent communication skills • Willingness to lead change and learn new skills • Creative and critical thinking abilities • Capable of coaching and supporting others • Seeks win-win solutions It should be noted that it is not required for a black belt to have a statistical or quality background. If they do not have this background they will receive it in the training. Most importantly the black belt must be able to lead a team, such that the project results are owned by key stakeholders after the black belt moves on to another project. To remain consistent with Lean principles, the

concept of JIT (Just In Time) training is used for all project team members, or the approach of �No project � No training.� Effective knowledge transfer requires participants to begin immediately using the tools they have learned. It is important to have an organizational transition plan associated with a deployment. Achieving the significant financial impact seen in other industries is not achieved by using Lean Six Sigma or Fusion Management as simply statistical measures or tools. It is achieved by affecting a culture change that is aligned with your organization�s strategy and vision.9, 11, 22, 23 While it may be the objective to change the culture and strategy of the company, it is necessary to go through an evolution. Starting to change the culture begins with completed projects that have documented and verified financial impact to the organization. Judicious selection of projects at the outset of the deployment is critical to building a foundation of credibility and success. While it is certainly acceptable to take on difficult and persistent problems, the project portfolio should be balanced such that the majority of projects are successful and completed in a timely manner. Some key factors for project selection and design are: • Align projects with business objectives • Prioritize projects based on effort, impact, risk, and

scalability to other areas • Establish management support for and

involvement in projects • Establish clear objectives and agreements through

project contracts • Commit the resources - particularly time and

training • Design the project team to engage key

stakeholders to ensure sustainability What follows is a sampling of projects successfully completed and financially verified in Asia and North America. Case History: Well Testing A large light crude oilfield in Southeast Asia uses a well testing process consisting of a portable mass-flow / density meter mounted on the back of a flat-bed truck. The trucks park next to the wells and connects to a manifold which allows diversion of the fluid thru the meter. Relative to the oil sales meters, the well testing process had historically over predicted oil production by about 30%. A Lean Six Sigma improvement team identified that the manually input water density for each well was the largest factor influencing the process. Existing water

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densities were found to have shifted over time due to water flood activities. New data was collected which resulted in a 22% improvement in accuracy. The result was an immediate reaction by the production engineer to wells with significant changes in measured production. During the same time period the team looked for ways to increase the frequency of well tests up to the customer specification of 1 test per well per week. By applying Lean tools over two improvement cycles the number of well tests increased 50%. Because the fixed cost of the 9 trucks and crews remained the same, the cost per test decreased in direct proportion without impacting quality. The data in Figure 3 example shows how improved quality can be accomplished simultaneously with reductions in cycle time and cost.

Figure 3 SE Asian Light Oil Well Test Accuracy

0.60

0.70

0.80

0.90

1.00

Jan-99 May-99 Sep-99 Jan-00 May-00 Sep-00 Jan-01 May-01 Sep-01

Rat

io (A

lloca

ted

/ The

oriti

cal)

Goal - Range 0.9 to 1.1

Stretch Goal - Range 0.95 to 1.05

Target 1.00

Start UpdatingWater Densities

1999 Average = 0.71

2001 Average = 0.91

Case History: Rod Pump Repair A large steam-drive oilfield in North America has over 8,500 active rod pumped wells. The pumps are the heart of the artificial lift system. A Lean Six Sigma project team utilized a number of the statistical and cycle-time tools to improve the rod pump design, pump repair and handling process. An intensive 2-day Lean action workout was undertaken to re-design the storage facility incorporating first-in first-out system, visual controls, and improvements in safety design. Improvements included: • The number of pump designs were reduced from

36 to 14. • Inventory dropped from 320 to 65 pumps. • Pump storage locations reduced from 9 to a single

centralized facility. • A new dedicated pump delivery service reduced

rig stand-by time. • Use of more insert pump designs reduced rig time

to pull pumps, improved lifting efficiency and based on historical data should pump increase run life, per Figure 4.

• The audited ISO 9001 quality rating of the pump repair shops improved.

• Optimal setting of the internal pump clearance to maximize pump run life was identified.

Figure 4

North American Heavy Oil Rod Pump Run Life

0

100

200

300

400

Pum

p R

un L

ife (d

ays)

Mean 353.8 384.0 245.7 145.4All Tubing Pumps All Insert Pumps All Slimhole Pumps All Pampa Pumps

Based on a sample of 32,000 pumps

Financial benefit was immediately realized in reduction of rig stand-by time and consumption of excess inventory. Control plans are in place to sustain the gain and the metrics are being monitored monthly to verify the predicted long-term improvements in pump run life. Figure 5, Below is another rod pump repair example from Asia where run life was improved by putting in place an ISO 9001 quality system and Lean improvements in the pump repair shop.

Figure 5

Run Life of Asian Rod Pumped Wells

100

200

300

400

500

600

Jan-98 Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02

Rod

Pum

p R

un L

ife (d

ays)

ISO 9001 Quality System Put into Rod Pump Repair Shop

Case History: Water Treating In this fluid treating example, a high water-cut oilfield produces about six million barrels of water per day. Small concentrations of oil are carried over from the separation tanks to the water flood injection wells. An improvement team identified that the oil skimming procedure, treating chemical pump reliability, and fluid distribution between the parallel trains of tanks were the major factors influencing the system performance. Business partners participated in a measurement system analysis which reduced the variability in the oil content measurement process. Two-sample hypothesis tests provided insight on sampling methods. To improve

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acceptance, plant operator representatives participated in the development of process flow diagrams and the SOPs. Following the execution of the new procedures, the field average oil carry-over was cut in half and the variability reduced significantly.

Figure 6 In a follow-up measure, a structured DOE (Design of Experiments) was conducted to quantify and model the impact of tank design, flow rate, and chemical concentration on oil-water separation. The results showed that all 3 factors were statistically significant, with chemical being the most important. The analysis identified optimal chemical treating levels. The improvement project produced significant annualized increase in revenue from oil recovery which were sustained as shown in Figure 6. Intangible benefits were realized in process knowledge, improved communication and acceptance of new procedures. Case History: Oil Treating

Figure 7

A North American heavy crude oil field uses horizontal heater treaters as the primary oil dehydration technique for the heavy crude, but produces an unacceptably high level of untreatable �slop� oil. A Lean Six Sigma project team studied the process and identified that the key input factors included flow surges, excessively high treating temperature, and selection of the oil treating chemical. After the new procedures were implementing the amount of �slop� oil was cut in half without increasing treating cost as shown in Figure 7. This resulted in an increase in revenue and a reduction in the cost of disposal of the waste product. Case History: Production Well Stimulation A Southeast Asian steam drive oil field had conducted more than 200 acid jobs to stimulate oil production wells. A Lean Six Sigma project using historical statistical data analysis revealed that the process did increase oil production and on an overall basis met economic hurdles. However, due to high process variability, a large percentage of wells did not produce an economic response that met economic hurdles. Use of hypothesis tests revealed that there were statistically significant differences in post-job oil gain due to following factors: • Area of the field • Production interval stimulated • Temperature of the well • Oil production before stimulation • Method of acid application The analysis also helped to overcome a paradigm that the volume of acid volume used, controlled post-job oil gain. The volume of acid was not found to produce statistically significant differences in oil gain. Changing the method of acid application resulted in a statistically significant increase in oil production as shown in Figure 8.

Figure 8

Comparison of Acid Placement Methods

22

3

2124

0102030405060708090

100

Method 1 Method 2 Method 3 Method 4

Job

Cou

nt o

r Oil

Gai

n B

OPD

Job Count Avg. Oil Gain Standard Deviation Oil Gain A decrease in well downtime required for well stimulation also occurred. Combined with the amount of acid used,

Oil Skim Rates

0

2500

5000

7500

Jan-

02

Mar

-02

May

-02

Jul-0

2

Sep-

02

Nov

-02

Jan-

03

Mar

-03

BPD

Start Stream 1 SOP Test

Start Stream 2SOP Test

Reissue SOP

0 10 20 30 40 50 60 70 80 90

After: Mean = 28 ppm StDev. = 9.7 ppm Before:

Mean = 42.5 ppm StDev. = 13.05 ppm

Oil in Water ppm

Oil Carry Over in Water Effluent

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a reduction was realized in the stimulation cost. By creating a detailed process flow diagram for screening, the stimulation prospects can better predict un-profitable jobs. Reduction of the number of acid stimulation work will lower cost and free resources while improving the economics for the overall process. Fewer acid jobs will reduce the generation of low pH fluid, which in turn reduces the number of upsets in the oil treating process and thus, reduces disposal costs. Case History: Production Logging Routine logging of steam injection wells is performed to determine the downhole split of the steam into multiple production zones. Surveys are used to make changes to the downhole assemblies controlling steam splits. A Lean Six Sigma team evaluated the logging process with a simple MSA (measurement system analysis) and found it to have an unacceptably high variance. The process was immediately discontinued pending additional evaluation. Unless a fix could be found the measurement process would have to be abandoned. A review of various procedures for log interpretation found that switching from a manual interpretation to a computer process dramatically reduced the variability. The process was retested and found to produce acceptable results, as shown in Figure 9.

Figure 9 The sigma level rating improved from a 0.4 to 2.2 after the change. The best practice was immediately deployed to all fields which use the method. A substantial financial benefit was realized in improved well work decisions.

Summary of Benefits Eleven Lean Six Sigma projects were completed in North American oilfields in 2002-2003. These projects yielded a net benefit of $500,000 per project. Based on

the success of the completed projects, fourteen new projects are currently active. In Southeast Asia oilfields 16 projects have been completed during 2001-2003. These projects yielded net benefit of $1,000,000 each. Based on the success of the completed projects fifteen new projects are approaching completion. Conclusions 1. Lean Six Sigma adapted from other industries and

ISO system concepts can be synergistically combined to improve business results in oilfield operations.

2. Systematic application of Lean Six Sigma and ISO systems provides a disciplined structure for gaining process knowledge and delivering business results safer, faster, better, and with lower cost. Process improvements have resulted in increased σlevels.

3. Lean Six Sigma does not offer any special short cuts to yielding financial benefits. The financial benefits are delivered through leadership, vision, sustained commitment, hard work, and the systematic application of quality and statistical tools.

4. Many of the tools and concepts used in Lean Six Sigma are not new to the oilfield. The discipline of the Lean Six Sigma framework can begin yielding business results in oilfield operations within one year by building on an organization�s existing knowledge of quality and statistics.

Acknowledgements The authors thank Gary Luquette, Warner Williams, Bob Galbraith, and Chris Prattini of ChevronTexaco management for their vision, support of this work and leadership of Lean Six Sigma. Nomenclature BOPD = Barrels Oil per Day BOPM = Barrels Oil per Month CE = Cause and Effect CNX = Constant, Noise, Experimental Cp = Process Capability Potential (unitless) Cpk = Process Capability (unitless) DFSS = Design for Six Sigma DMAIC = Define-Measure-Analyze-Improve-Control DOE = Design of Experiments JIT = Just in Time SL = Lower Specification Limit MSA = Measurement System Analysis OEE = Overall Equipment Effectiveness PCOR = Prioritize-Characterize-Optimize-Realize ROI = Return on Investment SIPOC � Supplier Input-Process Output Customer USL = Upper Specification Limit

Tracer Log Survey Results

0%10%20%30%40%50%60%70%80%90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Design %

Act

ual %

BeforeBefore AfterAfter

84434 9

xbar = process sample average σ = population standard deviation

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