Driving maximum production from active wells is the number one goal of every E&P company. When executives of a fast-growing, mid-size independent needed to bankroll exploration in a hot new play, they entrusted an Eagle Ford Asset Manager to boost production from their current assets.
The manager knew competitors in the area were reporting stronger returns, but he needed to understand how they were doing it and improve their approach. Speed and cost management were essential, so he couldn’t devote months to research or hire expensive outside analysts.
Using DI Pro Analytics and DI Transform tools, he and his team overcame the challenge by creating a new production plan that improved well productivity by more than 20 percent.
Quantify the Potential
Not all rock is created equal. The asset manager knew that defining peak productivity hinged on an accurate understanding of the quality of the acreage he held. He assigned his geology team to zero in on acreage with the greatest potential.
Drillinginfo geologists, geophysicists, engineers and data scientists have devoted years to creating a unique acreage grading methodology based on a “whole Earth” model incorporating more than 100 geological, rock property and PEI measurements and observations from thousands of play area wells.
With DI, the geology team was able to assess potential by reviewing an intuitive “heat map” to distinguish core vs. non-core holdings, rank the relative quality of each and identify where the greatest potential value existed.
Determining the value and potential of an asset is often an inexact science, limited to a cursory view of nearby acreage based on a handful of variables. This provides a very narrow basis for comparison, and can lead to false comparisons, unrealistic expectations and investment in all the wrong places.
Set the Right Benchmarks
The next step was to see how others were faring in comparable acreage. Different operators can go broke or make huge profits on the same land, so competitive benchmarking is crucial to identify who’s reaping the greatest rewards - and whose methods aren’t worth a second glance.
DI Analytics aggregates and analyzes data from thousands of wells and dozens of operators to quantify the range of results generated across acreage of various grades. The model is dynamic, with grading updated based on each new well drilled and the latest data reported.
Using DI’s normalized operator differential made it easy for the team to identify both outstanding and marginal performers. Furthermore, analysts used creaming curves to benchmark productivity using a variety of key metrics, including peak rates, short term cums and EURs.
Production data is widely available, but few operators have enough geologic data to attempt acreage comparisons on any significant scale. Even for the largest operators, conducting competitive benchmarking is typically a time-consuming, error-prone manual process that may involve hiring costly outside consultants and sacrificing profitability.
Without access to DI’s patented acreage grading models, even basic analysis can be cumbersome, leaving no resources to explore more sophisticated metrics and value-add analytics.
Identify Key Completion Factors
Having identified two competitors outperforming the company in similar acreage, the asset manager needed to understand their strategy. By zeroing in on the factors that contributed most, he could select the best completion techniques in the industry, combining his company’s areas of strength with best-in-class methods from others.
Drillinginfo captures well, wellbore, geological, stimulation and production data, and scrubs it multiple times to eliminate redundancies and anomalies, resulting in the “cleanest” data in the industry. DI data scientists then run monthly multivariate nonlinear “grounded truth” analytics to identify optimal, average, and ineffective practices for nearly every aspect of production, including well placement, lateral length, azimuth and more.
The asset manager’s team imported this “clean,” unaliased data, and used DI Transform tools to perform multivariate analytics comparing key variables, including cross-plotting performance numbers to operational factors like well length, azimuth, proppant total, fluid total and more.
Before any analysis could begin, team members would have to devote days to manually scrubbing data. For example, within a given dataset, the same operator might be identified in a dozen different ways due to alternate spellings, abbreviations, and acronyms.
Aliasing a single reservoir might take several days, and the risk of error rises with every hour of manual work. Shortcutting this process by skimping on statistical analysis is even less effective, as surface-level competitive comparisons can be misleading. For example, a competitor may be accelerating short-term production but diminishing EURs.
Optimize the Engineering Plan
After determining the most important factors, the asset manager tasked his engineering team with applying the newfound intelligence to the engineering plan, enabling the company to derive maximum value from every well, every time.
The engineers used DI Transform multivariate analytics to pinpoint the optimization points for each completion element across their own and their competitors’ practices. Using those optimization points as a foundation, they developed a better engineering plan. They then went a step further by applying cost information and exploring the return of different approaches so they could select the most effective engineering plan possible.
Diligent engineers can spend days importing their own project data into multivariate analytics software, but without completion data aggregated across multiple operators, defining optimization points is impossible. Even when data is compiled for more than one operator, manipulating models to assess the impact of a given variable - let alone the combined influence of multiple factors interacting in complex ways - oftentimes simply isn’t an option.
Pinpoint the Sweet Spot
With a fully optimized engineering plan in hand, the final step was to pinpoint the best placement for a new well.
With engineering factors normalized through DI Transform’s sophisticated modeling tools, the asset manager brought his geology team back to the table to create a refined, more accurate geological map of the play.
Using DI Transform’s massive capacity, the team was able to import seismic data and create a seismically derived view, enabling them to quantify the production impact of proximity to known faults, rock brittleness and more.
This made it possible to identify an area with tremendous potential that they hadn’t considered before. The asset manager took this recommendation forward, enabling the company to hit the sweet spot with their next well and deliver an immediate, significant boost to the company’s bottom line.
No other analytical software lets users integrate engineering and seismic data in a single view. Traditional models built manually using 2D maps, spreadsheets, and input from on-site technical teams simply can’t deliver the kinds of insights that allow Drillinginfo users to pinpoint the sweet spot with unprecedented precision and confidence.
The asset manager and his executives were pleased with the results. They were able to combine an optimized engineering plan with his geology team’s seismic data to improve short-term productivity at minimal cost, and pinpoint the best place to drill the next well for a rapid return.
Together, these actions boosted productivity by more than 20 percent, generating unprecedented profits to support the new exploration prioritized by company leadership and established a playbook for making the most of the company’s next big opportunity.
Contact a Drillinginfo specialist today to learn how DI Analytics and DI Transform could revolutionize your results, and request a personalized demo to experience the impact for yourself.