Impact of Stimulation Net to Gross Ratio on Hydraulic Fracture Deliverability in Unconventional Resources

Ganpule, Sameer; Waters, George A.; Al-Jalal, Ziad

doi:10.2118/173998-ms

Cited by 4 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microseismic activity generally indicates hydraulic fracture growth is through the complete Eagle Ford. Thus the presence of intervals prone to fracture conductivity loss appears to result in a productive fracture height being much less than the gross fracture height, as proposed by Ganpule et al 2015.…”

Section: Sres and Low Resolution At10) And A Static Borehole Image (Fmentioning

confidence: 78%

“…In addition, multiple examples exist of magma composition changing with continued eruption from a single volcano (e.g., Cebriá et al 2011;Gertisser and Keller, 2003;Moune et al 2012). This variability can be attributed to physical processes, such as restricted mobility of magma within the magma chamber, or chemical processes, such as fractional crystallization of the magma (Marsh, 2000).…”

Section: Factors Influencing Ash Bed Depositsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Diagenetic Alteration and Structural Integrity of Volcanic Ash Beds within the Eagle Ford Shale

Calvin

Diaz

Mossé

et al. 2015

Day 3 Thu, October 22, 2015

View full text Add to dashboard Cite

Ash deposits are found interbedded within organic mudstones such as the Vaca Muerta, Niobrara and Eagle Ford shales. Alteration of ash deposits interbedded within mudstones and shales have presented challenges for completion and production including pinching of fractures and swelling upon contact with drilling and completion fluids. This paper will review the mineralogic and petrologic variations in altered ash beds found within the Eagle Ford and examine the impact these chemical and physical properties can have on completion. The paper will also present a methodology for detection of ash beds using high-resolution measurements. Understanding and detecting the mineralogic variation between the altered ash beds will enable better completion and minimize risk during production.

show abstract

Section: Sres and Low Resolution At10) And A Static Borehole Image (Fmentioning

confidence: 78%

Section: Factors Influencing Ash Bed Depositsmentioning

confidence: 99%

Evaluating the Diagenetic Alteration and Structural Integrity of Volcanic Ash Beds within the Eagle Ford Shale

Calvin

Diaz

Mossé

et al. 2015

Day 3 Thu, October 22, 2015

View full text Add to dashboard Cite

show abstract

Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization

Davani

et al. 2020

SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

Data-driven decisions powered by machine-learning methods are increasing in popularity when it comes to optimizing field development in unconventional reservoirs. However, since well performance is impacted by many factors (e.g., geological characteristics, completion design, well design, etc.), the challenge is uncovering trends from all the noise. By leveraging basin-level knowledge captured by big data sculpting, integrating private and public data with the use of uncertainty quantification, Augmented AI (a combination of expert-based knowledge and advanced AI frameworks) can provide quick and science-based answers for well spacing and fracking optimization and assess the full potential of an asset in unconventional reservoirs. Augmented AI is artificial intelligence powered by engineering wisdom. The Augmented AI workflow starts with data sculpting, which includes information retrieval, data cleaning and standardization, and finally a smart, deep and systematic data QC. Feature engineering generates all the relevant parameters going into the machine learning model—over 50 features have been generated for this work and categorized. The final step is to perform model tuning and ensemble, evaluating the model robustness, generating model explanation and uncertainty quantification. Augmented AI adopts an iterative machine learning modeling approach. This approach combines new and innovative engineering and G&G workflows with data-driven models so that a deep understanding of the field behavior can be developed. Loops from feature selection to model tuning are used until good model results are achieved. The loop is automated using Bayesians optimization. All machine learning models have different strengths and weaknesses for prediction. Instead of manually determining which machine learning model to use, this approach uses an adaptive ensemble machine learning approach that is a stacking algorithm that combines multiple regression models via a second level machine learning model. It smartly aggregates opinions from different models with reduced variance and better robustness. Augmented AI has been applied in unconventional reservoirs with great results. A case study in Midland Basin is presented in this paper. Domain-induced feature engineering was performed to obtain important features for predicting well performance, and initial feature selection was conducted using feature correlation analysis. A trusted and explainable ML model was built and enhanced with uncertainty quantification. After running several sensitivity analyses, Augmented AI optimized the attributes of interest, then vetted the outcome, generating a report and visualizing the results. In addition, further information about the direct impact of well spacing on EUR was deconvoluted from other parameters using an ML explanation technique for Wolfcamp Formation in Permian Basin and subsequently well spacing optimization was presented for the case study in Midland Basin. An innovative model was created using Augmented AI to optimize well spacing, leveraging big data sculpting, domain and physics-induced feature engineering, and machine learning. The learning was transferred from the basin model to the specific region of interest. Augmented AI provides efficient and systematic private data organization, an explainable machine learning model, robust production forecast with quantified uncertainty and well spacing and frac parameters optimization. Augmented AI models are already built for major basins such as Midland and Delaware basins. The learning and knowledge of the model can be transferred to any region in a basin and can be refined using more accurate private data. This allows conclusions to be drawn even with a limited number of wells.

show abstract

Overflush and Fracturing: Playing Poker with your Completion

Rylance¹

2023

Day 2 Wed, February 01, 2023

View full text Add to dashboard Cite

Since the inception of hydraulic fracturing, the industry has wrestled with the concept of over/under- flushing, and has always pitched this as a binary philosophy, attempting to determine/define that this is either a fundamentally good or a bad approach. This schism simply grew with the extensive development of unconventionals; the use of overflush being an inherent and fundamental requirement for an effective and economic unconventional completion sequence. This paper will demonstrate that the true answer, as any engineer would expect, is that a detailed assessment is what is required and on a case-by-case basis. The paper will describe and reference several fracturing case histories, in both the Conventional and Unconventional environments, where the application of an overflush, an underflush or an engineered approach have been assessed or applied. Rather than taking an easy headline grabbing route to perpetuate the myth that the process is a major paradigm, or simply either a good or a bad thing. This paper will discuss some of the key aspects that impact the suitability of one approach over another; and how engineering the implementation can lead to a broader range of applicability/suitability for the most economically effective outcome. This includes an appreciation of the production/economic profile and exposure to risk, which is hugely different in say an ultra-deep-water five well offshore development vs. a field development with some 100s – 1,000s of wells in a lower-cost onshore environment. The paper will describe and demonstrate some of the fundamental variables that need to be considered; attempting to elaborate on a few of the key parameters which can influence the effective outcome. The paper will also indicate that there are several different scenarios whereby any form of overflush can result in a detrimental impact on the production rate and EUR, and that these must be fully appreciated. Subtleties, related to reservoir characteristics and fracture geometry; should be examined and appreciated. Additional aspects such as how the production, drawdown and pore-pressure will be managed can also have an impact. All these considerations, and more, will be discussed, described, and referenced. While there is no doubt that the overflush debate will continue unabated, the intent of this paper is to reduce the damage (or uncertainty), one way or the other, that will result. It is an attempt, at a minimum, to ensure that the debate becomes solely a technical one related to the approaches to be taken rather than a black and white one of right or wrong. Ultimately, the paper will advise, and inform, that the approach should be fully considered, engineered in detail and tailored to each and every application and that as a result is no longer simply considered a binary question.

show abstract

Impact of Stimulation Net to Gross Ratio on Hydraulic Fracture Deliverability in Unconventional Resources

Cited by 4 publications

References 17 publications

Evaluating the Diagenetic Alteration and Structural Integrity of Volcanic Ash Beds within the Eagle Ford Shale

Evaluating the Diagenetic Alteration and Structural Integrity of Volcanic Ash Beds within the Eagle Ford Shale

Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization

Overflush and Fracturing: Playing Poker with your Completion

Contact Info

Product

Resources

About