scholarly journals From oil field to geothermal reservoir: First assessment for geothermal utilization of two regionally extensive Devonian carbonate aquifers in Alberta, Canada

2017 ◽  
Author(s):  
Leandra M. Weydt ◽  
Claus-Dieter J. Heldmann ◽  
Hans G. Machel ◽  
Ingo Sass
Keyword(s):  
Geothermal Energy ◽  
Upper Devonian ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Carbonate Complex ◽  
Carbonate Aquifers ◽  
Promising Target ◽  
Porosity And Permeability ◽  
Alberta Basin ◽  
Drill Cores

Abstract. The Canadian Province of Alberta has the highest per capita CO2-equivalent emission of any jurisdiction in the world, predominantly due to industrial burning of coal for the generation of electricity and the mining operations in the oil sands deposits. Alberta’s geothermal potential could reduce CO2-emission by substituting at least some fossil fuels with geothermal energy. The Upper Devonian carbonate aquifer systems within the Alberta Basin are promising target formations for geothermal energy. To assess their geothermal reservoir potential, detailed knowledge of the thermo- and petrophysical rock properties is needed. An analogue study was conducted on two regionally extensive Devonian carbonate aquifers, the Southesk-Cairn Carbonate Complex and the Rimbey-Meadowbrook Reef Trend, to furnish a preliminary assessment of the potential for geothermal utilization. Samples taken from outcrops were used as analogue to equivalent formations in the reservoir and correlated with core samples of the reservoir. Analogue studies enable determination and correlation of facies related rock properties to identify sedimentary, diagenetic, and structural variations, allowing more reliable reservoir property prediction. Rock samples were taken from several outcrops of Upper Devonian carbonates in the Rocky Mountain Front Ranges as well as from four drill cores from the stratigraphically equivalent Leduc and three drill cores of the slightly younger Nisku Formation in the subsurface of the Alberta Basin. The samples were analyzed for several thermo- and petrophysical properties, i.e., thermal conductivity, thermal diffusivity and heat capacity, as well as density, porosity and permeability. Furthermore, open-file petrophysical core data retrieved from the AccuMap database were used for correlation. The results from both carbonate complexes indicate good reservoir conditions regarding geothermal utilization with an average reservoir porosity of about 8 %, average reservoir permeability between 10−12 and 10−14 m2, and relatively high thermal conductivities ranging from 3 to 5 W m−1 K−1. The most promising target reservoirs for hydrothermal utilisation are the completely dolomitized reef sections. The measured rock properties of the Leduc Formation in the subsurface show no significant differences between the Rimbey-Meadowbrook reef trend and the Southesk-Cairn Carbonate Complex. Differences between the dolomitized reef sections of the examined Leduc and Nisku Formation are also minor to insignificant, whereas the deeper basinal facies of the Nisku Formation differs significantly. In contrast, the outcrop analogue samples have lower porosity and permeability, likely caused by low-grade metamorphism and deformation during the Laramide Orogeny that formed the Rocky Mountains. As such, the outcrop analogues are no valid proxies for the buried reservoirs in the Alberta Basin. Taken together, all available data suggests that dolomitization enhanced the geothermal properties, but depositional patterns and other diagenetic events, e.g. fracturing, also played an important role. As for the development of the Devonian aquifers in the Alberta basin as geothermal reservoirs, repurposing abandoned oil and gas wells has the potential to produce geothermal energy cost efficiently, providing new business strategies.

scholarly journals From oil field to geothermal reservoir: assessment for geothermal utilization of two regionally extensive Devonian carbonate aquifers in Alberta, Canada

Solid Earth ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 953-983 ◽  
Author(s):  
Leandra M. Weydt ◽  
Claus-Dieter J. Heldmann ◽  
Hans G. Machel ◽  
Ingo Sass
Keyword(s):  
Upper Devonian ◽  
Oil Field ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Carbonate Complex ◽  
Carbonate Aquifers ◽  
Promising Target ◽  
Porosity And Permeability ◽  
Alberta Basin ◽  
Drill Cores

Abstract. The Canadian province of Alberta has one of the highest per capita CO2-equivalent emissions in Canada, predominantly due to the industrial burning of coal for the generation of electricity and mining operations in the oil sands deposits. Alberta's geothermal potential could reduce CO2 emissions by substituting at least some fossil fuels with geothermal energy.The Upper Devonian carbonate aquifer systems within the Alberta Basin are promising target formations for geothermal energy. To assess their geothermal reservoir potential, detailed knowledge of the thermophysical and petrophysical rock properties is needed. An analogue study was conducted on two regionally extensive Devonian carbonate aquifers, the Southesk-Cairn Carbonate Complex and the Rimbey-Meadowbrook Reef Trend, to furnish a preliminary assessment of the potential for geothermal utilization. Samples taken from outcrops were used as analogues to equivalent formations in the reservoir and correlated with core samples of the reservoir. Analogue studies enable the determination and correlation of facies-related rock properties to identify sedimentary, diagenetic, and structural variations, allowing for more reliable reservoir property prediction.Rock samples were taken from several outcrops of Upper Devonian carbonates in the Rocky Mountain Front Ranges and from four drill cores from the stratigraphically equivalent Leduc Formation and three drill cores of the slightly younger Nisku Formation in the subsurface of the Alberta Basin. The samples were analyzed for several thermophysical and petrophysical properties, i.e., thermal conductivity, thermal diffusivity, and heat capacity, as well as density, porosity, and permeability. Furthermore, open-file petrophysical core data retrieved from the AccuMap database were used for correlation.The results from both carbonate complexes indicate good reservoir conditions regarding geothermal utilization with an average reservoir porosity of about 8 %, average reservoir permeability between 10−12 and 10−15 m2, and relatively high thermal conductivities ranging from 3 to 5 W m−1 K−1. The most promising target reservoirs for hydrothermal utilisation are the completely dolomitized reef sections. The measured rock properties of the Leduc Formation in the subsurface show no significant differences between the Rimbey-Meadowbrook Reef Trend and the Southesk-Cairn Carbonate Complex. Differences between the dolomitized reef sections of the examined Leduc and Nisku Formation are also minor to insignificant, whereas the deeper basinal facies of the Nisku Formation differs significantly.In contrast, the outcrop analogue samples have lower porosity and permeability, likely caused by low-grade metamorphism and deformation during the Laramide orogeny that formed the Rocky Mountains. As such, the outcrop analogues are not valid proxies for the buried reservoirs in the Alberta Basin.Taken together, all available data suggest that dolomitization enhanced the geothermal properties, but depositional patterns and other diagenetic events, e.g., fracturing, also played an important role.

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

scholarly journals De-risking the energy transition by quantifying the uncertainties in fault stability

2021 ◽  
Author(s):  
David Healy ◽  
Stephen Hicks
Keyword(s):  
Geothermal Energy ◽  
Induced Seismicity ◽  
Energy Transition ◽  
Rock Properties ◽  
The Public ◽  
Fault Stability ◽  
Hazard And Risk ◽  
Synthetic Datasets ◽  
The Uk ◽  
Fracture Susceptibility

Abstract. The operations needed to decarbonise our energy systems increasingly involve faulted rocks in the subsurface. To manage the technical challenges presented by these rocks and the justifiable public concern over induced seismicity, we need to assess the risks. Widely used measures for fault stability, including slip and dilation tendency and fracture susceptibility, can be combined with Response Surface Methodology from engineering and Monte Carlo simulations to produce statistically viable ensembles for the analysis of probability. In this paper, we describe the implementation of this approach using custom-built open source Python code (pfs – probability of fault slip). The technique is then illustrated using two synthetic datasets and two case studies drawn from active or potential sites for geothermal energy in the UK, and discussed in the light of induced seismicity focal mechanisms. The analysis of probability highlights key gaps in our knowledge of the stress field, fluid pressures and rock properties. Scope exists to develop, integrate and exploit citizen science projects to generate more and better data, and simultaneously include the public in the necessary discussions about hazard and risk.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Factors Controlling Porosity Permeability Relationship for Reservoir Quality Prediction: A Case Study of Malay Basin Reservoir, Malaysia

2021 ◽  
Author(s):  
Fadzlin Hasani Kasim ◽  
Budi Priyatna Kantaatmadja ◽  
Wan Nur Wan M Zainudin ◽  
Amita Ali ◽  
Hasnol Hady Ismail ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Visual Analysis ◽  
Mineral Dissolution ◽  
Rock Properties ◽  
Reservoir Quality ◽  
Hydrocarbon Potential ◽  
Lower Grade ◽  
Reservoir Properties ◽  
Reservoir Evaluation ◽  
Porosity And Permeability

Abstract Predicting the spatial distribution of rock properties is the key to a successful reservoir evaluation for hydrocarbon potential. However, a reservoir with a complex environmental setting (e.g. shallow marine) becomes more challenging to be characterized due to variations of clay, grain size, compaction, cementation, and other diagenetic effects. The assumption of increasing permeability value with an increase of porosity may not be always the case in such an environment. This study aims to investigate factors controlling the porosity and permeability relationships at Lower J Reservoir of J20, J25, and J30, Malay Basin. Porosity permeability values from routine core analysis were plotted accordingly in four different sets which are: lithofacies based, stratigraphic members based, quartz volume-based, and grain-sized based, to investigate the trend in relating porosity and permeability distribution. Based on petrographical studies, the effect of grain sorting, mineral type, and diagenetic event on reservoir properties was investigated and characterized. The clay type and its morphology were analyzed using X-ray Diffractometer (XRD) and Spectral electron microscopy. Results from porosity and permeability cross-plot show that lithofacies type play a significant control on reservoir quality. It shows that most of the S1 and S2 located at top of the plot while lower grade lithofacies of S41, S42, and S43 distributed at the middle and lower zone of the plot. However, there are certain points of best and lower quality lithofacies not located in the theoretical area. The detailed analysis of petrographic studies shows that the diagenetic effect of cementation and clay coating destroys porosity while mineral dissolution improved porosity. A porosity permeability plot based on stratigraphic members showed that J20 points located at the top indicating less compaction effect to reservoir properties. J25 and J30 points were observed randomly distributed located at the middle and bottom zone suggesting that compaction has less effect on both J25 and J30 sands. Lithofacies description that was done by visual analysis through cores only may not correlate-able with rock properties. This is possibly due to the diagenetic effect which controls porosity and permeability cannot visually be seen at the core. By incorporating petrographical analysis results, the relationship between porosity, permeability, and lithofacies can be further improved for better reservoir characterization. The study might change the conventional concept that lower quality lithofacies does not have economic hydrocarbon potential and unlock more hydrocarbon-bearing reserves especially in these types of environmental settings.

scholarly journals Small-scale diagenetic facies heterogeneity controls porosity and permeability pattern in reservoir sandstones

2020 ◽  
Vol 79 (18) ◽  
Author(s):  
Matthias Heidsiek ◽  
Christoph Butscher ◽  
Philipp Blum ◽  
Cornelius Fischer
Keyword(s):  
Reservoir Rock ◽  
Rock Properties ◽  
Small Scale ◽  
Reservoir Quality ◽  
Permian Basin ◽  
Gas Reservoirs ◽  
Spatial Fluctuations ◽  
Porosity And Permeability ◽  
Reservoir Sandstones ◽  
Diagenetic Facies

Abstract The fluvial-aeolian Upper Rotliegend sandstones from the Bebertal outcrop (Flechtingen High, Germany) are the famous reservoir analog for the deeply buried Upper Rotliegend gas reservoirs of the Southern Permian Basin. While most diagenetic and reservoir quality investigations are conducted on a meter scale, there is an emerging consensus that significant reservoir heterogeneity is inherited from diagenetic complexity at smaller scales. In this study, we utilize information about diagenetic products and processes at the pore- and plug-scale and analyze their impact on the heterogeneity of porosity, permeability, and cement patterns. Eodiagenetic poikilitic calcite cements, illite/iron oxide grain coatings, and the amount of infiltrated clay are responsible for mm- to cm-scale reservoir heterogeneities in the Parchim formation of the Upper Rotliegend sandstones. Using the Petrel E&P software platform, spatial fluctuations and spatial variations of permeability, porosity, and calcite cements are modeled and compared, offering opportunities for predicting small-scale reservoir rock properties based on diagenetic constraints.

scholarly journals Key controls on the hydraulic properties of fault rocks in carbonates

2020 ◽  
pp. petgeo2020-034
Author(s):  
E. A. H. Michie ◽  
A. P. Cooke ◽  
I. Kaminskaite ◽  
J. C. Stead ◽  
G. E. Plenderleith ◽  
...  
Keyword(s):  
Carbonate Reservoirs ◽  
Rock Properties ◽  
Burial Depth ◽  
Extrinsic Factors ◽  
Fault Rock ◽  
Fault Rocks ◽  
Content Type ◽  
Intrinsic Factors ◽  
Hydraulic Behaviour ◽  
Porosity And Permeability

A significant knowledge gap exists when analysing and predicting the hydraulic behaviour of faults within carbonate reservoirs. To improve this, a large database of carbonate fault rock properties has been collected from 42 exposed faults, from seven countries. Faults analysed cut a range of lithofacies, tectonic histories, burial depths and displacements. Porosity and permeability measurements from c. 400 samples have been made, with the goal of identifying key controls on the flow properties of fault rocks in carbonates. Intrinsic and extrinsic factors have been examined, such as host lithofacies, juxtaposition, host porosity and permeability, tectonic regime, displacement, and maximum burial depth, as well as the depth at the time of faulting. The results indicate which factors may have had the most significant influence on fault rock permeability, improving our ability to predict the sealing or baffle behaviour of faults in carbonate reservoirs. Intrinsic factors, such as host porosity, permeability and texture, appear to play the most important role in fault rock development. Extrinsic factors, such as displacement and kinematics, have shown lesser or, in some instances, a negligible control on fault rock development. This conclusion is, however, subject to two research limitations: lack of sufficient data from similar lithofacies at different displacements, and a low number of samples from thrust regimes.Thematic collection: This article is part of the Fault and top seals collection available at: https://www.lyellcollection.org/cc/fault-and-top-seals-2019

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