Analysis and Interpretation of Haynesville Shale Subsurface Properties, Completion Variables, and Production Performance Using Ordination, a Multivariate Statistical Analysis Technique

Eburi, Simeon; Jones, Sid; Houston, Tim; Bonelli, James R.

doi:10.2118/170834-ms

Cited by 4 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geophysicists are turning to principal component analysis (PCA) and artificial neural networks to evaluate which combinations of attributes extracted from 3D seismic data best reflect hydrocarbon bearing reservoirs [99]. Additionally, development geologists and engineers use multivariate and artificial intelligence tools to understand which reservoir properties are most important in driving both production performance [100,101] and variability across hydrocarbon producing trends.…”

Section: Additional Uses Of Quantiative Biofacies Analysis/multivariate Statistical Toolsmentioning

confidence: 99%

Quantitative Biofacies Analysis to Identify Relationships and Refine Controls on Paleosol Development, Prince Creek Formation, North Slope Alaska, USA

Bonelli¹,

Flaig

2021

Geosciences

View full text Add to dashboard Cite

Late Cretaceous coastal plain deposits of the Prince Creek Formation (PCF) offer a rare glimpse into an ancient, high-latitude, arctic greenhouse ecosystem for which there is no modern analog. Here, we employ quantitative biofacies analysis to explore the spatio-temporal variability in PCF palynomorph and microbiota assemblages from nine paleosol horizons exposed along the Colville River, North Slope, Alaska. Biofacies results provide insight into paleoenvironmental controls on the coastal plain ecosystem. Cluster and ordination analyses recognize five biofacies and the following two assemblage types: (1) fern and moss dominated assemblages and (2) algae dominated assemblages. Ordination arrays biofacies along environmental gradients related to soil moisture and marine influence. Fern and moss dominated biofacies from regularly water-logged paleosols along lake and swamp margins on the lower delta plain clearly segregated from algae dominated assemblages of periodically drier levee-overbank paleosols. These results support previous interpretations from the sedimentology, paleopedology, and geochemistry of PCF paleosols that suggest that fluctuations in the water table, related to seasonal river discharge and variations in topography and drainage, controlled soil development and vegetation growth across the coastal plain. This quantitative biofacies-based approach provides an independent predictive tool and cross-check for interpreting environmental conditions along any ancient coastal ecosystem.

show abstract

Section: Additional Uses Of Quantiative Biofacies Analysis/multivariate Statistical Toolsmentioning

confidence: 99%

Quantitative Biofacies Analysis to Identify Relationships and Refine Controls on Paleosol Development, Prince Creek Formation, North Slope Alaska, USA

Bonelli¹,

Flaig

2021

Geosciences

View full text Add to dashboard Cite

show abstract

Development of a Powerful Data-Analysis Tool Using Nonparametric Smoothing Models To Identify Drillsites in Tight Shale Reservoirs With High Economic Potential

Cai

Liang

et al. 2017

SPE Journal

View full text Add to dashboard Cite

Summary The oil-and-gas industry is entering an era of “big data” because of the huge number of wells drilled with the rapid development of unconventional oil-and-gas reservoirs during the past decade. The massive amount of data generated presents a great opportunity for the industry to use data-analysis tools to help make informed decisions. The main challenge is the lack of the application of effective and efficient data-analysis tools to analyze and extract useful information for the decision-making process from the enormous amount of data available. In developing tight shale reservoirs, it is critical to have an optimal drilling strategy, thereby minimizing the risk of drilling in areas that would result in low-yield wells. The objective of this study is to develop an effective data-analysis tool capable of dealing with big and complicated data sets to identify hot zones in tight shale reservoirs with the potential to yield highly productive wells. The proposed tool is developed on the basis of nonparametric smoothing models, which are superior to the traditional multiple-linear-regression (MLR) models in both the predictive power and the ability to deal with nonlinear, higher-order variable interactions. This data-analysis tool is capable of handling one response variable and multiple predictor variables. To validate our tool, we used two real data sets—one with 249 tight oil horizontal wells from the Middle Bakken and the other with 2,064 shale gas horizontal wells from the Marcellus Shale. Results from the two case studies revealed that our tool not only can achieve much better predictive power than the traditional MLR models on identifying hot zones in the tight shale reservoirs but also can provide guidance on developing the optimal drilling and completion strategies (e.g., well length and depth, amount of proppant and water injected). By comparing results from the two data sets, we found that our tool can achieve model performance with the big data set (2,064 Marcellus wells) with only four predictor variables that is similar to that with the small data set (249 Bakken wells) with six predictor variables. This implies that, for big data sets, even with a limited number of available predictor variables, our tool can still be very effective in identifying hot zones that would yield highly productive wells. The data sets that we have access to in this study contain very limited completion, geological, and petrophysical information. Results from this study clearly demonstrated that the data-analysis tool is certainly powerful and flexible enough to take advantage of any additional engineering and geology data to allow the operators to gain insights on the impact of these factors on well performance.

show abstract

Development Strategy Optimization of Ning201 Block Longmaxi Shale Gas

Rui¹,

Wu²,

Shi³

et al. 2018

Day 3 Thu, October 18, 2018

View full text Add to dashboard Cite

Changning National Shale Gas Demonstration Area was established in 2012. Field Development of its Ning201 shale gas fields was geared up in 2014. By 2015, Changning area accomplished the construction of 15×108 m3/a production capacity. During early appraisal phase before 2014, average testing results of wells were 10~15×104 m3/d. In the end of 2016 and early 2017, average testing results of large numbers of wells across multiple pads achieved over 25×104 m3/d. Overall testing results of some pads have exceeded 180×104 m3/d per pad, and some of them may exceed 180×104 m3/d per pad in soon future. Through implementing geo-engineering integration, efficiency and effectiveness of field development have been steadily enhanced. This paper reviews our practices to establish and rapidly evolve the learning curve in this national demonstration area through geoengineering integration. Benefiting from geo-engineering integrated study, development options were systematically, proactively and effectively supported throughout the process of field development. Per the evolving understanding of reservoir and engineering challenges, adaptive technology and engineering solutions were exploited and optimized. A new geo-engineering model has been proposed which covers engineering applications of drilling, completion, production and development. Based on our gathered knowledge and experience, key contributing factors for marine shale gas development in Sichuan basin were thoroughly studied and summarized. They may include controlling factors of productivity, optimum production scheme, optimized well spacing, and so on. This paper also points out key areas for further improvement and enhancement in future.

show abstract

Analysis and Interpretation of Haynesville Shale Subsurface Properties, Completion Variables, and Production Performance Using Ordination, a Multivariate Statistical Analysis Technique

Cited by 4 publications

References 25 publications

Quantitative Biofacies Analysis to Identify Relationships and Refine Controls on Paleosol Development, Prince Creek Formation, North Slope Alaska, USA

Quantitative Biofacies Analysis to Identify Relationships and Refine Controls on Paleosol Development, Prince Creek Formation, North Slope Alaska, USA

Development of a Powerful Data-Analysis Tool Using Nonparametric Smoothing Models To Identify Drillsites in Tight Shale Reservoirs With High Economic Potential

Development Strategy Optimization of Ning201 Block Longmaxi Shale Gas

Contact Info

Product

Resources

About