New Methods of Determining Rock Properties for Geothermal Reservoir Characterization

2014 ◽  
pp. 37-40
Author(s):  
Mathias Nehler ◽  
Philipp Mielke ◽  
Greg Bignall ◽  
Ingo Sass
Keyword(s):  
Reservoir Characterization ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
New Methods

Characterisation of a highly heterogeneous geothermal reservoir based on geophysical well logs

2021 ◽  
Author(s):  
Johanna Bauer ◽  
Daniela Pfrang ◽  
Michael Krumbholz
Keyword(s):  
Reservoir Characterization ◽  
Sustainable Use ◽  
District Heating ◽  
Upper Jurassic ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Geophysical Logging ◽  
Matrix Permeability ◽  
Geophysical Well Logs

<p>For successful exploitation of geothermal reservoirs, temperature and transmissibility are key factors. The Molasse Basin in Germany is a region in which these requirements are frequently fulfilled. In particular, the Upper Jurassic Malm aquifer, which benefits from high permeability due to locally intense karstification, hosts a large number of successful geothermal projects. Most of these are located close to Munich and the “Stadtwerke München (SWM)” intends to use this potential to generate most of the district heating demands from geothermal plants by 2040.</p><p>We use geophysical logging data and sidewall cores to analyse the spatial distribution of reservoir properties that determine porosity, permeability, and temperature distribution. The data are derived from six deviated wells drilled from one well site. The reservoir rocks are separated by faults and lie in three different tectonic blocks. The datasets include image logs, GR, sonic velocities, temperature, flowmeter- and mud logs. We not only focus on correlations between rock porosity and matrix permeability, but also on how permeability provided by fractures and karstification correlate with inflow zones and reservoir temperature. In addition, we correlate individual parameters with respect to their lithology, dolomitisation and the rock’s image fabric type, adapted from Steiner and Böhm (2011).  </p><p>Our results show that fracture intensity and orientations vary strongly, between and within individual wells. However, we observed local trends between fracture systems and rock properties. For instance fracture intensities and v<sub>p</sub> velocities (implying lower porosities) are higher in rock sections classified as dolomites without bedding contacts. As these homogeneous-appearing dolomites increase, from N to S, the mean fracture intensities and v<sub>p</sub> velocities also increase. Furthermore, we observed most frequently substantial karstification in dolomites and dolomitic limestones. Nevertheless, an opposing trend for the percentage of substantial karstification can be also found, i.e., the amount of massive karstification is higher in the northern wells. The interpretation of flowmeter measurements show that the main inflow zones concentrate in those Upper Malm sections that are characterised by karstification and/or intense fracturing.</p><p>In the next step, we will correlate laboratory measurements of outcrop- and reservoir samples (e.g. porosity, permeability, and mechanical rock properties) with the logging data. The aim is to test the degree to which analogue samples can contribute to reservoir characterization in the Upper Jurassic Malm Aquifer (Bauer et al., 2017).</p><p>This work is carried out in the research project REgine "Geophysical-geological based reservoir engineering for deep-seated carbonates" and is financed by the German Federal Ministry for Economic Affairs and Energy (FKZ: 0324332B).</p><p>Bauer, J. F., Krumbholz, M., Meier, S., and Tanner, D. C.: Predictability of properties of a fractured geothermal reservoir: The opportunities and limitations of an outcrop analogue study, Geothermal Energy, 5, 24, https://doi.org/10.1186/s40517-017-0081-0, 2017.</p><p>Steiner, U., Böhm, F.: Lithofacies and Structure in Imagelogs of Carbonates and their Reservoir Implications in Southern Germany. Extended Abstract 1st Sustainable Earth Sciences Conference & Exhibition – Technologies for Sustainable Use of the Deep Sub-surface, Valencia, Spain, 8-11 November, 2011.</p>

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.

Application of structural interpretation and simultaneous inversion to reservoir characterization of the Duvernay Formation, Fox Creek, Alberta, Canada

2019 ◽  
Vol 38 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Ronald Weir ◽  
Don Lawton ◽  
Laurence Lines ◽  
Thomas Eyre ◽  
David Eaton
Keyword(s):  
Reservoir Characterization ◽  
Lateral Displacement ◽  
Rock Properties ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
Structural Interpretation ◽  
Simultaneous Inversion ◽  
Prestack Inversion ◽  
Unconventional Resource ◽  
Depth Relationship

Simultaneous prestack inversion of multicomponent 3D seismic data integrated with structural interpretation can provide an effective workflow to maximize value for unconventional plays. We outline an integrated workflow for characterizing the Duvernay play in western Canada, an emerging world-class low-permeability unconventional resource fairway. This workflow includes the determination of a time-depth relationship using synthetic seismograms, generation of seismic-derived time- and depth-converted structural maps, and calculation of inversion-based parameters of density and P- and S-wave velocity. The model-based procedure includes poststack (acoustic) inversion, amplitude variation with offset prestack inversion, and joint PP-PS inversion. With these rock properties determined, calculations are made to determine Young's modulus, Poisson's ratio, and brittleness. Faults are mapped based on time slices, isochrons, and correlatable vertical displacements of stratigraphic marker reflections. Significant strike-slip movements are identified by lateral displacement on interpreted geologic features, such as channels and reef edges. Seismic-derived attributes, combined with structural mapping, highlight zones that are conducive to hydraulic fracturing as well as areas unfavorable for development. Mapping of structural discontinuities provides a framework for understanding zones of preexisting weakness and induced-seismicity hazards.

Principles of Mathematical Petrophysics

2014 ◽  
Author(s):  
John H. Doveton
Keyword(s):  
Ad Hoc ◽  
Reservoir Characterization ◽  
Rock Properties ◽  
Mathematical Methods ◽  
Fluid Saturation ◽  
Mathematical Algorithms ◽  
Engineering Models ◽  
The Subject ◽  
Log Interpretation ◽  
Pore Types

The pioneering work of Gus Archie moved log interpretation into log analysis with the introduction of the equation that bears his name. Subsequent developments have mixed empiricism, physics, mathematical algorithms, and geological or engineering models as methods applied to petrophysical measurements in boreholes all over the world. Principles of Mathematical Petrophysics reviews the application of mathematics to petrophysics in a format that crystallizes the subject as a subdiscipline appropriate for the workstations of today. The subject matter is of wide interest to both academic and industrial professionals who work with subsurface data applied to energy, hydrology, and environmental issues. This book is the first of its kind, in that it addresses mathematical petrophysics as a distinct discipline. Other books in petrophysics are either extensive descriptions of tool design or interpretation techniques, typically in an ad hoc treatment. It covers mathematical methods that are applied to borehole and core petrophysical measurements to estimate rock properties of fluid saturation, pore types, permeability, mineralogy, facies, and reservoir characterization. These methods are demonstrated by a variety of case studies and summaries of applications. Principles of Mathematical Petrophysics is an invaluable resource for all people working with data related to petrophysics.

scholarly journals Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

2020 ◽  
Vol 146 ◽  
pp. 01003
Author(s):  
Vanessa Hébert ◽  
Thierry Porcher ◽  
Valentin Planes ◽  
Marie Léger ◽  
Anna Alperovich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Network Modelling ◽  
3D Images ◽  
Digital Core ◽  
Benchmark Datasets

To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.

Sign in / Sign up

Export Citation Format

Share Document