As mentioned in my previous post, in order to really be of value, we need to extend this analysis to future wells where we won't have such a complete data set. We need to build multi-variate models using common, "always" data – like pump curves, geologic maps, or bulk data. Our approach has been for engineers to build these models directly in Spotfire through a side panel we've added but save these models back to a central location so that they can be version controlled and accessed by anyone in the organization. They can quickly iterate through a variety of models trained on our data set to review the model performance and sensitivity. If we have access to historical information from previous wells, we can run our model on a variety of data sets to confirm its performance. This could be past wells that had micro seismic or where we knew there were issues with containment. Based on these diagnostics we can select a model to be applied by engineers on future developments. In order to make sure the model is used correctly we can set fences on the variables based on our training set to ensure the models are used appropriately. Because the models are built by your team – not a third-party vendor – they know exactly what assumptions and uncertainties went into the model. This approach empowers them to explore their data and answer questions without the stigma of a black-box recommendation.
Figure 1: Your team builds the models in Petro.ai – not a third-party vendor – so you know exactly what assumptions and uncertainties went into it. This approach empowers you to explore your data in new ways and answer questions without the limitations of black-box recommendations.
However, in addition to fences, we need to make sure engineers understand how and when to apply the models correctly. I won't go into this topic much but will just say that the direction our industry is moving requires a basic level of statistics and data science understanding by all engineers and geologists, because of this Ruths.ai has incorporated training into our standard engagements.
Slightly different hypothesis
This example used a variety of data, but it only answers one question. It's important to note that even slight variations in the question we ask can alter what data is needed. In our example, instead of asking if a specific frac design would stay within our selected interval, we wanted to know if the vertical fracture length changed over time, we would need a different data set. Since micro seismic is a snapshot in time we wouldn't know if the vertical frac stays open. A different data type would be needed to show these transient effects.
Data integration is often the biggest hurdle to analytics
We can start creating a map to tie back the required data needed for the questions we are interested in answering. The point of this diagram shown here is not to demonstrate the exact mapping of questions to data types, but rather, to illustrate how data integration quickly becomes a critical part of this story. This chart shows only a couple questions we may want to ask, and you can see how complicated the integration becomes. Not only are there additional questions, but new data types are constantly being added; none of which add value in isolation – there is no silver bullet, no one data type that will answer all our questions.
Figure 2: Data integration quickly becomes complicated based on the data types needed to build a robust model. There is no silver bullet. No single data type can answer all your questions.
With the pace of unconventional development, you probably don't have time to build dedicated applications and processes for each question. You need a flexible framework to approach this analysis. Getting to an answer cannot take 6 or 12 months, by then the questions have changed and the answers are no longer relevant.
Bringing these data types together and analyzing them to gain cross-silo insights is critical in moving from science to scale. This is where we will find step changes in completions design and asset development that will lead to improving the capital efficiency of unconventionals. I focused on completions today, but the same story applies across the well lifecycle. Understanding what's happening in artificial lift requires inputs from geology, drilling and completions. Petro.ai empowers asset teams to operationalize their data and start using it for analytics.
Three key take ways:
- Specific questions should dictate data collection requirements.
- Data integration is key to extracting meaningful answers.
- We need flexible tools that can operate at the speed of unconventionals.
I'm excited about the progress we've already made and the direction we're going.