An Integrated Approach for Optimizing Vertical Wolfbone Wells in the Delaware Basin

Yates, Malcolm; Sharma, Anoop; Itibrout, Tarik; Smith, Lauren; Fisher, Kelvin; Brown, Randy; Honeyman, Les; Bates, Bradley

doi:10.1190/urtec2013-071

Cited by 6 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Delaware Basin is one of the prolific basins in the USA comprising of stacked reservoirs which are the Wolfcamp shale, bone spring formation and Avalon shale. The Wolfcamp shale and bone spring formation are collectively called the "Wolfbone" play (Lohoefer et al 2014a, b;Lalehrokh and Bouma 2014;Sharma et al 2014;Yates et al 2013). The Delaware Basin is in South East New Mexico (Eddy, Chaves, and Lea County) and West Texas (Culberson, Pecos, Loving, Terrel, Reeves, Ward and Winkler County).…”

Section: Geology Of Formations and Data Preparationmentioning

confidence: 99%

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Wigwe

Bougre

Watson

et al. 2020

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Modern data analytic techniques, statistical and machine-learning algorithms have received widespread applications for solving oil and gas problems. As we face problems of parent–child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We accomplish this using a well-formulated spatio-temporal (ST) model. In this paper, we present a multi-basin study of production performance evaluation and applications of ST models for oil and gas data. We analyzed dataset from 10,077 horizontal wells from 2008 to 2019 in five unconventional formations in the USA: Bakken, Marcellus, Eagleford, Wolfcamp, and Bone Spring formations. We evaluated well production performance and performance of new completions over time. Results show increased productivity of oil and gas since 2008. Also, the Bakken wells performed better for the counties evaluated. We present two methods for fitting spatio-temporal models: fixed rank kriging and ST generalized additive models using thin plate and cubic regression splines as basis functions in the spline-based smooths. Results show a significant effect on production by the smooth term, accounting for between 60 and 95% of the variability in the six-month production. Overall, we saw a better production response to completions for the gas formations compared to oil-rich plays. The results highlight the benefits of spatio-temporal models in production prediction as it implicitly accounts for geology and technological changes with time.

show abstract

Section: Geology Of Formations and Data Preparationmentioning

confidence: 99%

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Wigwe

Bougre

Watson

et al. 2020

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

Rock physics of the Wolfcamp Formation, Delaware Basin

et al. 2019

View full text Add to dashboard Cite

Production from wells in organic-rich shales often shows considerable lateral variation. Reliable predrill methods to characterize the lateral heterogeneity of such reservoirs are required to optimize the trajectory of lateral wells in these low-permeability reservoirs. Petrophysical interpretation of measured well logs provides information on mineral, porosity, and kerogen content. Combining the results of petrophysical analysis with P-wave, S-wave, and density logs allows generation of a probability density function (PDF) for each of the different significant lithofacies. The PDFs are applied to the P- and S-impedance from prestack seismic amplitude variation with offset inversion to predict the spatial variation in the distribution of lithofacies and associated probability for the Wolfcamp Formation in an area covered by a 3D seismic survey in the Delaware Basin, West Texas. An anisotropic rock-physics model for the Wolfcamp Formation allows the effect of complex mineralogy, organic carbon concentration, and porosity on the P- and S-impedance to be investigated. Kerogen inclusions and pores act to increase Thomsen’s anisotropy parameter [Formula: see text] relative to [Formula: see text], and there is a competition between clay matrix anisotropy and inclusion shape anisotropy in determining the anisotropy of the rock. Inclusions with isotropic elastic properties act to decrease the anisotropy due to the dilution effect, but this decrease is partially offset by the increase in anisotropy due to the anisotropic shape of the inclusions. Application of the model to the determination of minimum horizontal stress indicates that kerogen-rich siliceous shales have the lowest value of minimum horizontal stress, whereas silica-rich calcareous shales, mixed siliceous shales, and clay-rich siliceous shales have higher values and may therefore act as barriers for the vertical growth of hydraulic fractures.

show abstract

From core analysis to log-based pay identification in the Delaware Basin Wolfcamp Formation

et al. 2015

View full text Add to dashboard Cite

Pay in tight-rock reservoirs is often associated with organic richness. The assumption is that the low permeabilities of the source rock do not allow for the migration of the hydrocarbon generated during the thermal maturation process. If core data are available, the water saturation, porosity, and total organic carbon (TOC) measurements can be used to confirm that the resource in place is correlated with the organic matter, which impacts the log characterization of pay. We studied the Wolfcamp A, Wolfcamp C, and Wolfcamp D tight-rock reservoirs using seven wells with core data through the Delaware Basin Wolfcamp Formation and discussed appropriate log-based pay identification methods. The linear relationship between TOC and original hydrocarbon in place (OHIP) for samples in the Wolfcamp C and D intervals validated the hypothesis of a self-sourced reservoir. We adopted the well-established correlation between TOC and formation bulk density to identify the better part of the Wolfcamp C and D reservoirs. Our core data suggested to use a bulk density of [Formula: see text] or less to define pay. The lack of trend in the scatterplot of TOC and OHIP for samples in the Wolfcamp A interval indicated that the hydrocarbon had probably migrated, at least on a local scale. In this case, pay could not be identified by log techniques developed to calculate TOC in organic-rich rocks. Instead, we built an OHIP model based on a quad combo logging suite using an ensemble learning method. This model favorably compared with a TOC-based pay flag against production logging data from two vertical producers with stages through the Wolfcamp A interval.

show abstract

An Integrated Approach for Optimizing Vertical Wolfbone Wells in the Delaware Basin

Cited by 6 publications

References 1 publication

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Rock physics of the Wolfcamp Formation, Delaware Basin

From core analysis to log-based pay identification in the Delaware Basin Wolfcamp Formation

Contact Info

Product

Resources

About