Augmenting Asset Teams:  Beware Fragmented Decision Making
Data Science & Analytics

Augmenting Asset Teams: Beware Fragmented Decision Making

Rosemary Jackson  •  

Fragmented decision making in shale: Interdependent variables are analyzed separately which results in isolated frames of reference that can’t consolidate the subsurface geomechanical intricacies of hydraulic fracturing.

Petro.ai Scenario Analysis: Modeling reports that pull together the information needed, consolidating your pad and completions optimization calculations to choose your best economic scenario.

Asset management scattered across numerous software programs leads to fragmented decision making. Teams are fed pieces of information that are not blended cohesively. And decisions have to be made faster than the speed of drilling. You don’t need one more software tool. What would be great is to have your options known quickly and clearly. With your tradeoff possibilities laid out in front of you, you can drive those decisions home with timely confidence. That’s why results-oriented processes like Petro.ai Pad Scenario Analysis (PSA) and Completions Scenario Analysis (CSA) are making a big difference in the oil and gas industry today.

Dr. Brendon Hall, VP of Geoscience, explains, “We’ve found that many asset teams don’t have data scientists on staff to leverage their data to answer pertinent questions quickly.

“What we keep hearing is, can Petro.ai do this analysis, run these tools and tell us what the answers are?Then we as an asset team can come in and use Petro.ai to look at those results, analyze them and incorporate them into our business decisions.

“Responding to that, Petro.ai created Pad Scenario Analysis (PSA) and Completions Scenario Analysis (CSA).

“PSA and CSA are Petro.ai reports that use your data and your experiences to inform the AI engine. These are a defined project for important steps in the pad design where you have specific or general questions. The modeling reports are a compilation of tradeoff scenarios that manage adjusting design factors and/or variable quantities and the economic impact of shifting those constraints.

unconventional shale gunbarrel view

“Let’s look at the Pad Scenario Analysis package that Petro.ai offers to augment the asset team. Our first step is to work with an asset team to understand those constraints that they’re experiencing in their pad development process. Are there zones they’re targeting, have they encountered problems in areas or what do they think the ideal spacing might be? After defining the parameters, the Pad Scenario Analysis looks at tradeoffs between well spacing and sequencing, well placement, parent/child wells that may have already drained sections of the reservoir and the impact on ROI (return on investment).

Then we build the models with them, they can QC them at every step and interrogate them to see what’s going on. It’s definitely not a black box. We work with the asset team to make sure it’s accurate according to their definitions and behaving in a sensible way.

A key feature or a key variable that derives the notion of tradeoffs is drainage. That’s really the element that’s underlying how closely you can put wells together, space them apart and effectively drain the reservoir.

“The Pad Scenario Analysis is an exercise where we work collaboratively with the asset team to integrate their data into the Petro.ai platform and build data driven physics constrained models. And then running a sensitivity analysis so that we can get a good understanding of the impact of all these variables on economics.”

sensitivity of proppant/ft on NPV

“More recently we’ve introduced another modeling report which fits in a bit later in the pad development workflow. This is the Completions Scenario Analysis. In a very similar way, we work with the asset team to integrate all of their data especially as much completions relevant data as we can. So far, we’ve looked at a lot of proppant and fluid intensity. The idea is to figure out what the impact of those completions design parameters are on economics.

“It’s expensive to put proppant into the ground. Sometimes these pads are designed just by looking at a single successful story where someone put a quantity of proppant into the ground, maybe 3000 lbs/ft and had a good result. Well, then people just copy that without analyzing if 2500 lbs/ft could have been better. You can save a lot of money if you can shave off a couple hundred pounds in these completions intensities.

“The Completions Scenario Analysis builds a model that lets us look at the impact of the completions intensity on economics.

“It takes all the economic factors into account to look at the optimized economic return on, for instance, different proppant loadings. What we’ve found successfully is that you can reduce the amount of proppant you use and not affect the amount of oil that’s produced which means, simply, you save a lot. Your overall return is higher.

This PSA and CSA journey for me has shown how critical drainage is as a variable when building these models. It’s really the link that ties the geoscience world to the engineering and the productivity world. We consistently find that it’s a very important variable. The main thing that determines drainage is geomechanics and having the framework that can integrate all of that together is key. You can’t just build a multivariate model and expect to get high accuracy. You really need to take it all the way from the geology of the rock through engineering to economics. You need a framework that can bring all of that data together.

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