Sweet Spots: What Are They, Where Are They, How Are They Created and Are They Important Anyway?

Tennant, Steven Hunter; Giles, M. R.

doi:10.2118/167760-ms

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…Different scales may be used to locate sweet spots in hydrocarbon reserves. Seismic interpretation methods, such as the discovery of stratigraphic or structural traps, may be used to locate sweet spots at the field scale [14]. Sweet spot identification along the well involves locating areas with appropriate petrophysical properties connected to in-place hydrocarbon volume and formation conductivity, such as formation thickness, rock porosity, fluid saturation, rock permeability, rock relative permeability, and rock wettability.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised machine learning technique for classifying production zones in unconventional reservoirs

Abbas

Gharavi

Hindi

et al. 2023

International Journal of Intelligent Networks

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Section: Introductionmentioning

confidence: 99%

Unsupervised machine learning technique for classifying production zones in unconventional reservoirs

Abbas

Gharavi

Hindi

et al. 2023

International Journal of Intelligent Networks

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Reservoir characterization for sweet spot detection using color transformation overlay scheme

Ismail

Raza

Gholami

et al. 2020

J Petrol Explor Prod Technol

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Reservoir characterization carried out for the quantitative representation of a reservoir leads to effectively managing the hydrocarbon reservoir. There have been few attempts made to characterize the reservoir and assess the potential of a reservoir for the sweet spots using seismic, geomechanical, and petrophysical characteristics where a combination of seismic and petrophysical characters is often ignored given the complexity of such integration. To the best of our knowledge, none of the studies have provided overall reservoir characterization workflows using the combination of petrophysical and geomechanical attributes for sweet spot detection. This study aims to adopt a new color transformation overlay scheme to assess the potential of reservoirs by identifying the sweet spots that can guide to propose a location for a new well. The core scale measurements and well log interpretation were used to determine the petrophysical and geomechanical properties of a highly complex sandstone reservoir which were used as the input to build a 3D reservoir model. The results obtained indicated that predicting the distribution of sweet spots is an effective scheme for well-planning and field development in the region. Four sweet spots are identified in the studied reservoir based on the color transformation overlay approach which has heterogeneous continuity behavior in vertical and horizontal directions. The identification of sweet spots based on top view is not enough for the complete understanding of spreading in all directions. Cross-sectional analysis of the color transformation overlay model revealed that there is a sweet spot near Well-E in west which has ideal reservoir potential and place to consider another proposed well.

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Integrating Machine Learning in Identifying Sweet Spots in Unconventional Formations

Tandon

2019

SPE Western Regional Meeting

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Productive zones or "sweet spots" in unconventional reservoirs depend on their geomechanical and petro-physical rock properties. Machine learning algorithms can significantly improve workflows used for evaluating sweet-spots in such complex reservoirs. The objectives of this paper are to: (i) quantity the effects of rock mechanical properties on fracturing treatments using data analytics and (ii) use regression-based machine learning algorithms and improve sweet-spot assessment in complex mudrock reservoirs. We used a hydraulic fracturing simulator that couples fluid-flow with fracture deformation in discrete fracture networks to model field-scale hydraulic fracturing treatments. First, we selected several geomechanical properties related to rock fracability. We obtained wide variation in aforementioned properties using a quasi-random design approach. Then, we performed 200 slick-water fracturing simulations with quasi-random distribution of design parameters using the hydraulic fracturing simulator. We quantified the performance of fracture treatments by calculating the effective short- and long-term Stimulated Reservoir Volume of the reservoir (SRV). We finally analyzed the results of numerical simulations by applying regression analysis to improve the assessment of sweet-spots in complex reservoirs. The regression analysis involved the following simulation variables: shear modulus, poisson's ratio, fracture friction coefficient, principal horizontal stress anisotropy, fracture toughness, fracture closure stress, shear dilation angle, and initial fracture aperture. The SRV results were analyzed using: linear regression, linear regression with beta coefficients, ridge and lasso regression, and principal component regression algorithms. The regression analysis revealed that linear models can explain 73.1% and 59.2% variance in short- and long-term SRV values, respectively. The ridge and lasso regression and beta linear regression analysis revealed that stress anisotropy, fracture dilation angle, and fracture friction coefficient show the highest effect on the aforementioned SRV values. In all the regression models, shear modulus and critical fracture toughness did not have a significant effect on SRV but these parameters are important as they are correlated to other parameters that directly impact fluid flow. The results of using data analytic approaches demonstrated that factors related to unpropped fracture conductivity play a critical role in success of hydraulic fracturing treatments. We have also introduced and compared the performances of different machine learning algorithms that might be used to assess the impact of geomechanical properties on fracturing treatments. Such supervised and unsupervised machine learning algorithms can help in integrating legacy field data in the analysis of productive zones in complex reservoirs. Such analysis can also be used to develop data-based models that might improve the study of sweet-spot and fracturing treatment performance assessment in complex reservoirs.

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Sweet Spots: What Are They, Where Are They, How Are They Created and Are They Important Anyway?

Cited by 6 publications

References 2 publications

Unsupervised machine learning technique for classifying production zones in unconventional reservoirs

Unsupervised machine learning technique for classifying production zones in unconventional reservoirs

Reservoir characterization for sweet spot detection using color transformation overlay scheme

Integrating Machine Learning in Identifying Sweet Spots in Unconventional Formations

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