Petroelastic and geomechanical classification of lithologic facies in the Marcellus Shale

2015 ◽  
Vol 3 (1) ◽  
pp. SA51-SA63 ◽  
Author(s):  
Dario Grana ◽  
Kristen Schlanser ◽  
Erin Campbell-Stone
Keyword(s):  
Marcellus Shale ◽  
Expectation Maximization Algorithm ◽  
Rock Physics ◽  
Rock Properties ◽  
Geomechanical Properties ◽  
Facies Classification ◽  
Well Location ◽  
Input Variables ◽  
Shale Oil Reservoirs ◽  
The Impact

Log-facies classification at the well location allows determination of the number of facies, the facies definition, and the correlation between facies and rock properties along the well profile. In unconventional reservoirs, because of the necessity for hydraulic fracturing in shale gas and shale oil reservoirs, facies classification should account for petroelastic and geomechanical properties. We developed a facies classification methodology based on the expectation-maximization algorithm, a statistical method that allows finding the most likely facies classification and the associated probability distribution, given the set of geophysical measurements in the borehole. We applied the proposed workflow to a complete set of well logs from the Marcellus shale and developed the corresponding facies classification from log properties measured and computed in three different domains: petrophysics, rock physics, and geomechanics. In thne preliminary well-log and rock-physics analysis, we identify three main lithofacies: limestone, shale, and sandstone. The application of the classification method provided the vertical sequence of the three lithofacies and their pointwise probability of occurrence. A sensitivity analysis was finally evaluated to investigate the impact of the number of input variables on the classification and the effects of cementation and kerogen.

Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. WA45-WA63 ◽  
Author(s):  
Dario Grana ◽  
Marco Pirrone ◽  
Tapan Mukerji
Keyword(s):  
Reservoir Characterization ◽  
Uncertainty Propagation ◽  
Rock Physics ◽  
Integrated Approach ◽  
Physical Models ◽  
Well Log ◽  
Petrophysical Properties ◽  
Formation Evaluation ◽  
Facies Classification ◽  
Well Location

Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The final output is the set of petrophysical curves with the associated uncertainty, the profile of the facies probabilities, and the entropy, or degree of confusion, related to the most probable facies profile. The full statistical approach allows us to propagate the uncertainty from data measured at the well location to the estimated petrophysical curves and facies profiles. We applied the proposed methodology to two different well-log studies to determine its applicability, the advantages of the new integrated approach, and the value of uncertainty analysis.

scholarly journals Geomechanical Properties of Unconventional Shale Reservoirs

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad O. Eshkalak ◽  
Shahab D. Mohaghegh ◽  
Soodabeh Esmaili
Keyword(s):  
Energy Demand ◽  
Marcellus Shale ◽  
Well Logs ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Rock Properties ◽  
Real Field ◽  
Full Field ◽  
Geomechanical Properties ◽  
Data Driven Modeling

Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using general geostatistical methods coupled with data-driven modeling. The results showed that synthetic geomechanical well logs and real field logs fall into each other when the input dataset has not seen the real field well logs. Geomechanical distributions of the Marcellus shale improved significantly when full-field data is incorporated in the geostatistical calculations.

Digital reconstruction of reservoir sandstones to predict hydraulic and mechanical rock properties

2021 ◽  
Author(s):  
Maria Wetzel ◽  
Thomas Kempka ◽  
Michael Kühn
Keyword(s):  
Numerical Simulations ◽  
Mineral Content ◽  
Practical Importance ◽  
Rock Physics ◽  
Mineral Dissolution ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Reservoir Sandstones ◽  
The Impact

<p>Quantifying trends in hydraulic and mechanical properties of reservoir sandstones has a wide practical importance for many applications related to geological subsurface utilization. In that regard, predicting macroscopic rock properties requires detailed information on their microstructure [1]. In order to fundamentally understand the pore-scale processes governing the rock behaviour, digital rock physics represents a powerful and flexible approach to investigate essential rock property relations [2]. This was shown, e.g., for hydraulic effects of anhydrite cement in the Bentheim sandstone in relation to an unsuccessful drilling campaign at the geothermal well Allermöhe, Germany [3]. Rock weakening due to decreasing calcite mineral content was also demonstrated by application of numerical simulations [4]. </p><p>In the present study, a process-based method is used for reconstructing the full 3D microstructure of three typical reservoir reference rocks: the Fontainebleau, Berea and Bentheim sandstones. For that purpose, grains are initially deposited under the influence of gravity and afterwards diagenetically consolidated. The resulting evolution in porosity, permeability and rock stiffness is examined and compared to the respective micro-CT scans of the sandstones. The presented approach enables to efficiently generate synthetic sandstone samples over a broad range of porosities, comprising the microstructural complexity of natural rocks and considering any desired size, sorting and shape of grains. In view of a virtual laboratory, these synthetic samples can be further altered to examine the impact of mineral dissolution and/or precipitation as well as fracturing on various petrophysical correlations, what is of particular relevance for a sustainable exploration and utilisation of the geological subsurface.</p><p>[1] Wetzel M., Kempka T., Kühn M. (2017): Predicting macroscopic elastic rock properties requires detailed information on microstructure. Energy Procedia, 125, 561-570. DOI: 10.1016/j.egypro.2017.08.195 <br>[2] Wetzel M., Kempka T., Kühn M. (2020): Hydraulic and mechanical impacts of pore space alterations within a sandstone quantified by a flow velocity-dependent precipitation approach. Materials, 13, 4, 3100. DOI: 10.3390/ma13143100<br>[3] Wetzel M., Kempka T., Kühn M. (2020): Digital rock physics approach to simulate hydraulic effects of anhydrite cement in Bentheim sandstone. Advances in Geosciences, 54, 33-39. DOI: 10.5194/adgeo-54-33-2020 <br>[4] Wetzel M., Kempka T., Kühn M. (2018): Quantifying rock weakening due to decreasing calcite mineral content by numerical simulations. Materials, 11, 542. DOI: 10.3390/ma11040542 </p>

scholarly journals Environmental Sensitivity and Awareness as Differentiating Factors in the Purchase Decision-Making Process in the Smartphone Industry—Case of Polish Consumers

2021 ◽  
Vol 13 (1) ◽  
pp. 348
Author(s):  
Lukasz Skowron ◽  
Monika Sak-Skowron
Keyword(s):  
Expectation Maximization ◽  
Cross Validation ◽  
Expectation Maximization Algorithm ◽  
Decision Making Process ◽  
Environmental Sensitivity ◽  
Significance Level ◽  
On Line ◽  
Purchase Process ◽  
The Impact ◽  
Fold Cross Validation

The first of the research objectives discussed in this article was to analyze the differences related to the valuation of particular factors influencing the purchase process in the smartphone industry, expressed by respondents with different sensitivity and environmental awareness, as well as the assessment of their knowledge about the impact of smartphones on the natural environment. The second objective of the research was to determine whether the level of environmental sensitivity, awareness and knowledge about the impact of smartphones on the environment has a statistically significant influence on the respondents’ choice of smartphone brand. The survey was conducted using an on-line questionnaire, distributed by a specialized research agency on a representative sample of over 1000 Polish residents. In order to identify the various customers clusters, the expectation-maximization algorithm and the v-fold cross-validation were used. Additionally, in order to analyze the significance level of differences between clusters the nonparametric Mann-Whitney U-test was carried out. The results show unequivocally that people with a different approach to ecological issues demonstrate statistically significant differences in their purchasing behaviors in the smartphone industry. Furthermore, it was noticed that in the case of comparing some smartphones brands, there is a statistically confirmed difference in the environmental sensitivity and awareness of the customers who use them. Moreover, the research has shown that in Polish customers’ consciousness smartphones are mistakenly considered to be relatively safe and environmentally friendly products.

scholarly journals The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 322
Author(s):  
Evelina Volpe ◽  
Luca Ciabatta ◽  
Diana Salciarini ◽  
Stefania Camici ◽  
Elisabetta Cattoni ◽  
...  
Keyword(s):  
Probability Density ◽  
Central Italy ◽  
Model Performance ◽  
Probability Density Functions ◽  
Slope Instability ◽  
Complex Nature ◽  
Density Functions ◽  
Umbria Region ◽  
Input Variables ◽  
The Impact

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

The Impact of Rock Properties on Acid Jetting in Carbonate Rocks: An Experimental Study

2016 ◽  
Author(s):  
V. Ndonhong ◽  
E. Belostrino ◽  
D. Zhu ◽  
A. D. Hill ◽  
R. E. Beckham ◽  
...  
Keyword(s):  
Experimental Study ◽  
Carbonate Rocks ◽  
Rock Properties ◽  
The Impact

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

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