Challenge:

Being a member of sub-surface team working on Shale oil Assets our prime motive was to  enhance production and optimize the completions design of our gas wells. Being in an ever  evolving arena a lot of different experiments on completions design were conducted by our  company for span of about 5 years. Enhancing well and field level production being the key  objective, task was to look at different parameters of well design already used in past and  reach to a most optimum design.

Selection:

Having gone through several design thinking activities/tools, so we are ready to try the learning  launch tool. Broadly speaking, the learning launch is an experiment meant to quickly test an  idea and prototype.

Application

Our team had several brainstorming sessions to address the challenge described above, given  the fact that we were needed to build a working model to link various completion parameters  with the production and test the hypothesis in the upcoming wells we just had about 2 months to  zero in on an optimum design, we decided to take help of Multivariate Analytics for screening  of key performance drivers. We held several workshops with all the stake holders as well as  higher management to fully understand the challenge at hand and their perspectives about it.  Being versed with Analytical tools in excel/Python/R we tried to build a model that links Gas  production with the key performance drivers. The first step in this pursuit was to identify and  screen the performance drivers but before that we had to cleanse and condition our voluminous  data which was an arduous task in itself. Finally, together we could build a working model by  using mutually independent variables to predict the performance of the wells.

The next phase was testing of hypothesis. Here we wanted to fine tune our model to create a  robust predictive tool. For this purpose, we blind tested our wells from different generations of  completion design. Finally, after an exhaustive and iterative process our model was ready to  be used for optimization and predictions within a tolerance of +/- 10%.

Insight:

For our first learning launch, we focused on the primary elements and variables which impacts  performance of the wells. We have worked through the historical data and quantified the  impact of the primary variables on the performance of the wells. Based on the first learning  launch we changed the completion design and flow back strategy of the wells to get better  performance of the wells. Once our primary variables / assumptions were worked out, we  moved to second phase of learning launch where we started to identify secondary variable  which will play role in performance of the wells, however, there impact will be lesser compared  to primary variables. To quantify the impact of secondary variables we went through Multi  variate analysis and generated the mathematical model. Based on the mathematical model, we  started fine tuning the completion design and flow back strategies in the wells which further  helped in performance enhancement of the wells.

Our actual results were slightly different from what we anticipated linked to continuously  changing commodity price environment which will play pivotal role in well economics.  Our team  is open to the economic prioritization of problem, which we will review and incorporate it into  future launch designs.

Approach:

Our team has already competed the technical optimization of the process to enhance the  productivity of the wells using design thinking tools and learning launch is the best tool at the  moment. As previously mentioned, our team needs to review the insight gained from our first  two launches and incorporate this economic insight into future launch designs. Future launch  designs should reflect insight gained, progress made, and come closer to a compelling solution  ready for full rollout.

