John Chaplin is a Technical Advisor at Novi where he applies his background in A&D evaluations khổng lồ ensure Novi"s predictive analytics và economics help customers develop better decisions in the field. Prior lớn Novi, John held various roles in A&D groups at SIPC, Merrill Lynch và Morgan Stanley.

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Introduction: Problem we are trying to solve; methodology we are applying to lớn solve sầu it

We have written a couple of posts about optimizing completions on DUC (Drilled but UnCompleted) well inventory given the extreme constraints on capital that Operators are facing. In our first post, we focused on QEP’s inventory in the Midland, & then in a second post, NBL’s inventory on the Delaware side. In both of those cases, the DUC inventory was relatively concentrated in a couple of development units.

We wanted to take a look at DUCs that were a bit more spread out, so we took at look at XEC, which has a vast acreage position in the Delaware spanning across a large swatch of the northern Delaware basin. The vastness of their position is due khổng lồ some degree khổng lồ their acquisition of Resolute Energy, which closed in March of 2019.

XEC has 50+ DUCs according khổng lồ Enverus, spread across a very wide swath of the Delaware, some in the super deep column on the Texas/New Mexico border and running south from there, and some more out khổng lồ the western fringe of the basin. Given this spread, the interaction between spacing, stimulation design, & subsurface makes the question of optimization a double edged sword – one must take into trương mục how confident any model is in its predictions as you move from the thickest column in the core lớn the thinnest in the fringe, while at the same time understanding which completion designs applied to the DUCs are going khổng lồ drive the best possible returns in the short term while minimally compromising EURs.

In this machine learning use case, we will demonstrate the utility of Novi’s implementation of Shapley values to understand interactions between well spacing, stimulation and subsurface data. Shapley values are based on the work of mathematician and economist Lloyd Shapley, which have their roots in game theory. Shapley’s work was later adapted for large scale adoption in the machine learning world by a couple of University of Washington students . Shapley data will be applied lớn help determine which completion thiết kế choices across Cimarex’s (XEC) DUC position in the Delaware basin would drive the most accretive value at a variety of oil strip price decks.

In addition, and perhaps more important when oil companies are operating on negative sầu margins with a strip price in the $đôi mươi range, we will utilize Novi’s confidence intervals to guide recommendations on completion designs across XECs DUC well inventory, so the confidence the Novi Mã Sản Phẩm has in any given prediction is clear when evaluating the financial implications of each decision.

Where is XEC’s DUC inventory?

~50 DUCs spread out across the entire basin for Cimarex that we will run a study on.

We loaded DUC locations for Cimarex in the Delaware basin into Forecast Engine and Spotfire. There are 50+ DUCs split between several units across their position. We are going to run these DUCs at 6 different completion designs khổng lồ see if we can gain insights on which designs should be run at each location.

Historically, Cimarex has employed one completion kiến thiết on their inventory:

XEC 2,500 lbs/ft (0.8 Fluid): 2,500 lbs/ft of proppant & 2,000 gals/ft of fluid

We will take this completion kiến thiết và flex it on the fluid ratio going up lớn what offphối operators are doing in the area at a 1.0 & 1.2 fluid ratio. We will also flex the proppant by increasing và decreasing by 500 lbs/ft off of Cimarex’s thiết kế.

XEC 1,500 lbs/ft (0.8 Fluid) : 1,500 lbs/ft of proppant & 1,200 gals/ft of fluid XEC 2,000 lbs/ft (0.8 Fluid) : 2,000 lbs/ft of proppant & 1,600 gals/ft of fluid XEC 2,500 lbs/ft (1.0 Fluid) : 2,500 lbs/ft of proppant and 2,500 gals/ft of fluid XEC 2,500 lbs/ft (1.2 Fluid) : 2,500 lbs/ft of proppant & 3,000 gals/ft of fluid XEC 3,000 lbs/ft (0.8 Fluid) : 3,000 lbs/ft of proppant & 2,400 gals/ft of fluid

Maximizing 2 year cums with completions optimization

After running XEC’s DUC inventory through Novi Forecast Engine at the 6 different completion designs from above, we can begin to gain insight inlớn what is driving the production of these wells. Comparing the 2-Yr Oil cumulative volumes across the 6 designs, the Novi Mã Sản Phẩm predicted that the larger proppant loading increased the volumes; not too surprising. More interesting that that – increasing the fluid ratio provides minimal impact on the wells performance, as you can see by the flatness of the middle part of curve sầu below. Doubling the job from a 1,500 lbs/ft to 3,000 lbs/ft analysis increases 2-Year oil cum by 6.4 bbls/ft or a ~21% increase. These are valuable insights but only enough lớn gain a general view of the inventory.

Increasing proppant has large impact to expected oil production though the impact begins to diminish when you go beyond 2,500 lbs/ft. Increasing the fluid ratio only improves oil production slightly.

Once we dive inlớn the Novi Shapley data generated for each well at 30 day prediction intervals we can begin khổng lồ see what features are driving the individual predictions of these DUC inventory wells. Based on this, we can find opportunities to optimize completions kiến thiết even further. In this context, we will use the Novi Shapley data khổng lồ gather information on the model’s predicted contribution of the well’s features to the given production of the well…e.g. how much does proppant contribute to the wells overall prediction relative sầu khổng lồ the other well data? Similarly, we can evaluate spacing & other Mã Sản Phẩm data inputs.

12 of Novi’s proprietary spacing features were chosen as having high-importance in the Novi model based on Enverus data, along with 5 geosúc tích features and 2 operational features.

For example, looking at the geongắn gọn xúc tích features such as thickness & depth of the Wolfcamp A-XY we see two relationships emerge:

As the thickness of Wolfcamp A-XY increases the relative percentage of increase in 2-Yr cumulative sầu oil volume given the larger completion job begins to increase. This is shown by the delta between the xanh dots (larger proppant loading) và the green dots (smaller proppant loading).As the depth of the Wolfcamp A-XY decreases, the expected production from the wells begins to decrease as much as 20% from the Mã Sản Phẩm dataset average. The slope of the two lines signifies the different relationships the thickness of the reservoir has when using two different proppant loading volumes.
Depth matters for both completion designs và the relationship remains constant no matter how much proppant is used.

Novi Shapley data also provides valuable insight inkhổng lồ the impact of well spacing. As an example, Novi calculates Closest Lateral Distance for every well in the training dataphối as the distance in feet from the closest lateral to that well. This was one of the more influential Novi Spacing features in terms of explaining production variance in the training wells.

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A couple of things become evident when evaluating the Novi Shapley values for the Closest Lateral Distance datapoint:

Unbounded wells, which are vertically orientated in the chart below at 5,000 feet, have an expected uplift in production for the larger completion kiến thiết that make sense. The variance in well productivity at the larger job size are likely due to subsurface conditions.As the Closest Lateral Distance decreases to 1,500 feet or lower, you can see from the chart below that the Novi Model believes that larger completion job has a diminishing uplift as spacing is tighter. It is particularly ađáng yêu at very tight spacing.Looking at fluid loading ratgame ios in the second chart below, you can see that as Closest Lateral Spacing decreases, there is a very rapid decline in production uplift associated with the fluid intensity ratio relative sầu khổng lồ proppant. This suggests that increasing fluid ratio relative lớn proppant will very likely not be economically viable, depending on where in the basin you are.
Similar declines in expected production in both completion techniques insinuates that the relationship is constant at multiple proppant volumes.
Larger slopes for increasing fluid ratio signify a negative sầu relationship with closest lateral distance.

Risking returns based on Model confidence

When creating prediction mix outputs in Novi Forecast Engine, one can select two different confidence intervals. We submit that the relative confidence you have in any prediction is extremely important information in evaluating the best solution — engineers can trip themselves (and multi-billion projects) up when they fail to take into lớn tài khoản uncertainty. In the example below, we requested that Novi Forecast Engine output the P90 /10 predictions khổng lồ get an understanding of the model’s confidence, as well as the P35/P65. These choices are configurable with each Prediction Set you create with Novi Forecast Engine.

With each well receiving a prediction at each Confidence Interval (as well as the mặc định, which is P50) we can Reviews the distribution of the results between the bands & determine by the tightness of the projections how confident the Model is in the P50 prediction. The tighter the ratio of P90 to lớn P10, the more confident the Novi Mã Sản Phẩm is in that prediction. This is shown in the second chart below for a single well case.

Novi Forecast Engine allows for the output of 4 confidence interval projections in addition lớn the P50.
With multiple projections for each well at several confidence intervals users can quickly risk production by choosing lower confidence interval projections or adjusting their P50 predictions by the ratio of the intervals.

We can determine two things from evaluating the P90/P10 confidence ratio:

Across the XEC DUC inventory, the model is most confident in the 2,500 lbs/ft job with a 1.2 fluid ratio (first chart below).The model is more confident in predictions for wells in the “Core” of the play, versus predictions made for wells in the fringe (second chart below – lower numbers are better).
Across the whole DUC inventory the most prominent design in the dataset is the most confident prediction.
Ability lớn understvà the model’s confidence in it’s prediction allows for engineers & business planners to lớn better risk their expected production & understvà the riskier assets in their inventory. Lower number = more model confidence.

When we optimize the P50 output for IP7đôi mươi oil we see that for most wells the most optimal kiến thiết is 3,000 lbs/ft job.

However, if we handicap the Novi recommendation based on the relative sầu confidence the model has in each prediction (the P90/P10 ratio), the recommendations change substantially.

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To see these outputs in more detail watch the video clip below where we walk through the analysis and risk adjust completion recommendation for XEC’s DUC inventory.