Roger Field

Barrow Island (BWI), 56 km from the coast of Western Australia, is home to several mature reservoirs that have produced oil since 1965. The main reservoir is the Windalia sandstone, and it has been waterflooded since 1967, while all the other reservoirs are under primary depletion. Due to the maturity of the asset, it is economically critical to continue to maximise oil production rates from the 430 online, artificially lifted wells. It is not an easy task to rank well stimulation opportunities and streamline their execution. To this end, the BWI Subsurface Team applied Lean Six Sigma processes to identify opportunities, increase efficiency and reduce waste relating to well stimulation and well performance improvement.

The Lean Sigma methodology is a combination of "Lean Production" and "Six Sigma" these are methods used to minimise waste and reduce variability respectively. The methods are used globally in many industries, especially those involved in manufacturing. In this asset, we applied the processes specifically to well performance improvement through stimulation and other means. The team broadly focused on categorising opportunities in both production and injection wells and ranking them, specifically: descaling wells, matrix acidising, sucker rod optimisation, reperforating and proactive workovers. The process for performing each type of job was mapped and bottlenecks in each process isolated.

Upon entering "Control" phase, several opportunities had been identified and put in place. Substantial improvements were made to the procurement, logistics and storage of hydrochloric acid (HCl) and associated additives, enabling quicker execution of stimulation work. A new programme was also developed to stimulate wells that had recently failed and were already awaiting workover, which reduced costs. A database containing the stimulation opportunities available at each individual well assisted with this process. The project resulted in the stimulation of several wells in the asset with sizable oil rate increases in each.

This case study will extend the information available within the oil-industry literature regarding the application of Lean Sigma to producing assets. It will assist other Operators when evaluating well stimulation opportunities in their fields. Technical information will be shared regarding feasibility studies (laboratory compatibility work and well transient testing results) for acid stimulation and steps that can be taken to streamline the execution of such work. Some insights will also be shared regarding the most efficient manner to plan rig-work regarding stimulation workovers.

It has been a year since my last lecture/rant about corporate shortsightedness. I am sure my concerns were taken to heart and everyone was able to spend the last year working on training, documenting best practices, tracking failure root causes and costs, and optimizing lift efficiency. This allows me to focus on a problem unique to the extraction industries. This problem arises because we work at the transition from unbound, heterogeneous nature to bound, homogeneous controlled environments. Our problem is that we are dealing with multiphase, multicomponent fluids whose compositions change spatially and temporally inside imperfectly understood heterogeneous reservoirs whose characteristics also change spatially and temporally.

It has been a year since my last lecture/rant about corporate shortsightedness. I am sure my concerns were taken to heart and everyone was able to spend the last year working on training, documenting best practices, tracking failure root causes and costs, and optimizing lift efficiency. This allows me to focus on a problem unique to the extraction industries. This problem arises because we work at the transition from unbound, heterogeneous nature to bound, homogeneous controlled environments. Our problem is that we are dealing with multiphase, multicomponent fluids whose compositions change spatially and temporally inside imperfectly understood heterogeneous reservoirs whose characteristics also change spatially and temporally.

Our problem is that we are dealing with multiphase, multicomponent fluids whose compositions change spatially and temporally inside imperfectly understood heterogeneous reservoirs whose characteristics also change spatially and temporally.

Overview

It has been a year since my last lecture/ rant about corporate shortsightedness. I am sure my concerns were taken to heart and everyone was able to spend the last year working on training, documenting best practices, tracking failure root causes and costs, and optimizing lift efficiency. This allows me to focus on a problem unique to the extraction industries. This problem arises because we work at the transition from unbound, heterogeneous nature to bound, homogeneous controlled environments.

Our problem is that we are dealing with multiphase, multicomponent fluids whose compositions change spatially and temporally inside imperfectly understood heterogeneous reservoirs whose characteristics also change spatially and temporally. When a black-oil correlation comes with the caveat that the results are ±20%, they are not kidding. And when they specify a range of validity that we blithely exceed, all bets are off. When we take Darcy’s law for linear flow of water through a homogeneous sandpack and manipulate it to apply to multiphase flow through a heterogeneous multilayer reservoir, we have to make assumptions. Sure, we can break the reservoir into a grid and assign values to each grid, but the truth is we do not know with certainty what the reservoir properties are outside of near-wellbore regions. We use algorithms to come up with grid properties, but we are really just making educated guesses.

What are we to do? The most common approach is to plug in our best-guess data and use the results as truth. New engineers feel a sense of accomplishment for a job well done. More experienced engineers hope for the best. Old codgers like me hope nothing catches on fire.

We see the results of this uncertainty all around us. Wells do not produce as expected, equipment runs are inconsistent, reserves estimates are constantly revised.

A better approach is to recognize that we are working in a world of uncertainty. At a minimum, create a worst case, a best case, and a most-likely case. Many do this, but they do not always appreciate that the only thing for certain is that the most-likely case will not happen. With any luck at all, the results will fall somewhere between the worst and best cases. A bigger problem is the temptation to seize upon the best case because it is the only one that will meet the fiscal requirements for the project. It is easy for me to tell you to just say no, but people have emotional buy-in to their projects and often feel that their career depends upon the project going forward.

What we should do is use error analysis and statistical methods to define uncertainty. The propagation of uncertainty through mathematical equations can be calculated. It was required in my physical-chemistry lab almost 40 years ago. We can calculate statistically expected results. It is nothing new and is well-understood. We just do not apply it to our own uncertainty. As engineers, we should demand or create software that allows input data to be defined as either a value and uncertainty or a range and distribution. Results should be given either a measure of the uncertainty or a distribution.

I selected these papers because the authors have recognized, embraced, and accepted uncertainty. The papers describe how they addressed uncertainty to find a solution for their problems. JPT

Recommended additional reading at OnePetro: www.onepetro.org.

OTC 24799 A New Model for the Accurate Prediction of Critical Liquid Removal Based on Energy Balance by Xiao-Hua Tan, Southwest Petroleum University, et al.

SPE 175310 Improving the Electrical Submersible Pump’s Operational Time by 50% Using the Six-Sigma Procedures by M. Ahmad, Kuwait Institute for Scientific Research, et al.

SPE 176194 The Success Story of the Light-North Area of Roger Block: Continuous Exertion To Increase Electrical- Submersible-Pump Performance Through Rectifying Design Process To Resolve MDSS Problem by Cintani Kusuma Dewi, Chevron Pacific Indonesia, et al.