# NMR contribution for porosity-permeability heterogeneity characterization in limestones: implications for 3D reservoir prediction and flow simulation in a world class geothermal aquifer

Maxime Catinat, Benjamin Brigaud, Marc Fleury, Miklos Antics, Pierre Ungemach, Mélanie Davaux, Julien Gasser Dorado, Hadrien Thomas, Codjo Thomas Florent Essou, Simon Andrieu, et al.

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Maxime Catinat, Benjamin Brigaud, Marc Fleury, Miklos Antics, Pierre Ungemach, et al. NMR contribution for porosity-permeability heterogeneity characterization in limestones: implications for 3D reservoir prediction and flow simulation in a world class geothermal aquifer. European Geothermal Congress 2022, European Geothermal Energy Council, Oct 2022, Berlin, Germany. hal-05294983

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# NMR contribution for porosity-permeability heterogeneity characterization in limestones: implications for 3D reservoir prediction and flow simulation in a world class geothermal aquifer

Maxime Catinat<fnref n="1" />, Benjamin Brigaud<fnref n="1" />, Marc Fleury<fnref n="2" />, Miklos Antics<fnref n="3" />, Pierre Ungemach<fnref n="3" />, Mélanie Davaux<fnref n="3" />, Julien Gasser Dorado<fnref n="3" />, Hadrien Thomas<fnref n="1" />, Codjo Thomas Florent Essou<fnref n="1" />,<fnref n="3" />, Simon Andrieu<fnref n="4" />, Emmanuel Mouche<fnref n="5" />

Maxime.catinat@universite-paris-saclay.fr

**Keywords:** Carbonate, Dogger, Paris basin, NMR, Modelling, Flow simulation

## ABSTRACT

The Paris suburban areas concentrate ca fifty geothermal district heating (GDH) systems that recover the heat of a 1.5 to 2 km deep aquifer, the Bathonian limestones, which stand among the most productive aquifer worldwide dedicated to geothermal district heating. Despite this highly dependable geothermal resource, the current 70 MWh yearly development rate will not meet the objective set in the French energy climate strategy ambitioning a 6.4 TWh production in 2023 which would require a six-to-ten-fold higher rate. Hence, optimizing and mastering the French geothermal energy problematic stand as a major challenge for France at large, and more specifically the Ile-de-France region which hosts a population nearing 12 million inhabitants. This paper aims to reconstruct and simulate heat and fluid flow transfer in the Paris Basin by implementing an innovative methodology addressing (i) facies, porosity, and permeability heterogeneities, and (ii) modelling, simulating and predicting regional hydrothermal flow and reservoir performance at given locations.

The study focuses on a large area nearing 1400 km<fnref n="2" /> around Paris, concentrating 116 geothermal wells (59 producers, 57 injectors) and 31 oil and gas exploration and production wells.

In the city of Cachan, a sub horizontal GDH doublet has been drilled in 2018, a world premiere in geothermal well architecture, seeking enhanced well productivities in medium to low permeability reservoir areas which

otherwise would have remained unchallenged. Nuclear Magnetic Resonance logs (T₂, NMR) have been recorded on the injector well (GCAH2), providing reliable information on pore size distributions (microporosity vs macroporosity) and permeabilities. More recently in 2021, 2 new doublets (GBD1/GBD2 and GBD3/GBD4) were drilled in Bobigny where the same NMR logging tools were deployed for both GBD1 and GBD3 production wells providing once again valuable information in terms of vertical permeability distribution within the reservoir.

In addition, a variety of wireline logging data ranging from gamma-ray to density, resistivity, sonic and neutron porosity have been compiled from the 147 wells. Thirty-six meters of core section and 170 thin sections have been observed for facies description. A total of 10 facies, grouped into four facies associations, have been identified over 128 wells according to depths. Twelve 3rd order stratigraphic sequences have been identified.

The 3D grid modelled is composed of 100 m x 100 m cells in the XY domain. To ensure a high vertical resolution of the reservoir heterogeneities, the vertical Z size, which depends on the subzonal thicknesses, is averaging 1m, thus leading to a 3D grid with over 14.10⁶ cells.

A convergent interpolation method was used to build horizons from defined stratigraphic surfaces, and further to upscaling, the facies have been extrapolated using the “Truncated Gaussian with trends” algorithm. The “Gaussian Random Function Simulation” algorithm was used to populate the entire grid with

<footnote n="1">¹ Université Paris-Saclay, GEOPS, CNRS, 91405 Orsay, France</footnote>

<footnote n="2">² IFP Energies Nouvelles, 1-4 Avenue de Bois-Préau, 92852 Rueil-Malmaison, France</footnote>

<footnote n="3">³ GEOFLUID, 165 Rue de la belle étoile, 95700 Roissy CDG, France</footnote>

<footnote n="4">⁴ BRGM, 3 Avenue Claude Guillemin, 45100 Orléans, France</footnote>

<footnote n="5">⁵ Université Paris-Saclay, CNRS, CEA, UVSQ, Laboratoire des Sciences du Climat et de l'Environnement, 91191, Gif-sur-Yvette, France</footnote>
petrophysical input properties including some 4 000 NMR well logging data (GCAH, GBD1 and GBD3 wells), 72 NMR experimental data derived from core samples (GBD4 well) and porosity-permeability data derived from core plugs. The most attractive reservoir properties for geothermal exploitation are located within the shoal oolitic grainstone deposits exhibiting porosity and permeability values averaging 12.5 % and 100 mD respectively.

In perspective, heat transfers and fluid flows will be
simulated via the Pumaflow™ software to assess the
risks induced by interfering doublets and provide
guidelines to future de-risking operations.

# 1. INTRODUCTION

The growing demand for renewable energy is a global
challenge. One of the solutions for achieving the energy
transition is to mobilize the earth's heat by developing
low, medium or high enthalpy geothermal energy.

In France, the primary production of renewable
energies in 2020 reached 322 TWh of which only 2
TWh were produced by the geothermal industry
(Ministère de la transition écologique, 2021).

To increase this contribution, the Middle Jurassic
(Dogger) carbonate reservoirs are identified as the most
promising development target for geothermal energy in
the Paris basin since the 1980s (Housse, Maget, 1976;
Antics et al., 2005.; Lopez et al., 2010; Allo et al.,
2021).

Such reservoirs are characterized by heterogeneous and
complex pore networks resulting from a large
depositional environment variability and conditions,
such as depositional energy, water depth, climate or
tectonics, and diagenetic processes which can lead to an
enhancement or reduction of reservoir properties
(Lucia, 1995, 1999, 1983; Moore, 2001, 1989; Ronchi
et al., 2010; Goodner et al., 2020).

This pore-space heterogeneity results in a high porosity
and permeability variability as it remains difficult to
accurately capture such spatial distribution of
properties at reservoir scale (Ahr, 2008; Hollis et al.,
2010; Rebelle and Lalanne, 2014; Yarmohammadi et
al., 2020).

To improve the understanding of reservoir
heterogeneities distribution, recent geothermal
operations have been conducted at Bobigny and Cachan
sites (Paris region) in France where nuclear Magnetic
Resonance (NMR) logging tool was deployed (GCAH2
injector well and both GBD1 and GBD3 production
wells), including a 36-meter core-section of the
geothermal reservoir at well GBD4.

Spreading out in open hole, NMR logging tool provides
crucial data such as continuous porosity (φ),
permeability (k), pore-size distribution and fluid
saturation in rocks (Kenyon, 1997).

In addition to NMR logs and experimental core
measurements, various standard well-logs such as
gamma-ray, neutron porosity, resistivity, density, sonic
as well as data acquired during production tests
(temperature, pressure, flowmetry) were compiled to
study area and then integrated in Petrel©,
Schlumberger software, to build a 3D static geological
and petrophysical model of the reservoir.

Despite a few permeability plug measurements for
geothermal wells owing to high coring cost, reservoir
permeability estimation derived from CMR logging
tool appears as a key parameter for reservoir modelling.

## 2. GEOLOGICAL & GEOTHERMAL SETTING

The Paris basin is a large intracratonic basin, stable and
poorly tectonised, covering a vast part of northern
France of 110 000 km² bounded by crystalline massifs
(the Ardennes, the Vosges, the Armorican and central
massifs) and filled with about 3000 m of sediments
from the Triassic to Quaternary formations
(Guillocheau et al., 2000; Mégnien, 1980).

In the central part of the basin, the 200 m-thick Middle
Jurassic limestones are located approximately at a
depth of 1500 m TVD (True Vertical Depth), and
especially Bathonian deposits are ones of the
preferential targeted aquifers for geothermal
exploitation.

During Bathonian times, the Paris basin was covered by
a warm and turbulent epicontinental sea in subtropical
latitudes. Such climatic conditions favoured the
development of so-called western Armorican and
Burgundian shallow marine carbonate platforms
(Contini and Mangold, 1980; Brigaud et al., 2014;
Vincent et al., 2021).

From the most internal lagoonal facies to the most
external lower offshore facies, Bathonian deposits can
be divided into three different geological units that can
be related along a theoretical ramp carbonate profile : (1)
Comblanchian unit, (2) Oolithe Blanche unit and
finally the (3) cyclical marls-limestones alternations
unit (Lopez et al., 2010).

The Oolithe Blanche formation in the basin centre is
known as displaying the best reservoir properties as
shown by the past few years geothermal developments
(Hamm, 2015).

In the Paris region, the vast majority of geothermal
operations mobilising doublet technology reach the
Middle Jurassic limestones, featuring a high
geothermal potential with fluid temperatures that can
reach 80°C in the deepest areas and mainly supplying
heating networks in densely urbanized and populated
areas (Bonté et al., 2010; Lopez et al., 2010, Lund and
Toth, 2021), (Fig. 1A).
## 3. MATERIALS & METHODS

A digital database has been created including all the well heads, well paths and well logs from 116 geothermal and 31 exploration and production oil wells located in the study area (Fig. 1B). A total of 57 neutron porosity (NPHI), 127 gamma-ray (GR), 72 sonic (DT), 51 density (RHOB) and 49 resistivity (LLD-LLS) logs as well as 86 temperature, pressure and flowmeter logs acquired with Production Logging Tools (PLT) have been compiled and integrated in Petrel® as input parameters for reservoir characterization.

Previous porosity and permeability data measured on core plugs from exploration and producing oil wells have been collected and added to a pre-existing database including regional porosity and permeability dataset.

Facies were coded manually according to cuttings (170 thin sections), core observations and from masterlogs and old geological reports on 128 wells. A single vertical geothermal well (GAY1), drilled in the late eighties in the North of Paris, was thoroughly investigated, including 153 meters of cores that could been retrieved. Sedimentological descriptions made from these core observations were used as a reference for geological classification. The GBD4 core samples collected were also used to produce thin sections that were submitted to further geological descriptions. Petrophysical investigations such as water permeability and NMR measurements were also conducted on these samples to assess the reservoir pore-structure.

A total of 10 facies has been monitored and grouped into 4 different facies associations. Granular reservoir facies were discretized with a higher vertical resolution based on the water-bearing productive layers identified via Production Logging Tools (PLT). Reservoir geometry was conceptualized following a sequence stratigraphic framework according to the previous work of Thomas (2021). The reservoir is divided into twelve sequential stratigraphic horizons corresponding to maximum regressive surfaces and maximum flooding surfaces. Thirteen inter-wells correlations (stratigraphic cross-sections) have been reconstructed to accurately identify facies heterogeneity from 2D perpendicular transects (Fig. 1C).

<figure><img src="image_3.png" /></figure>

**Figure 1:** (A) Location map of the Ile-de-France region. Modelling perimeter is described in red. Exploration and production oil wells are represented by black dots. Each geothermal well, according to the targeted geological formation, is represented by a coloured dot. (B) Kriged temperature map at the top of the Bathonian reservoir. (C) Transect location map in the study area modelling perimeter to proceed to inter-well correlation within the reservoir.
The 3D static model is divided into twelve zones. Each zone is sub-divided into numerous thin 1 cell-layers according to the resolution needed to meet facies definition which were discretized in 3D using the ‘most of’ upscaling method, meaning that the most representative facies in terms of proportions is assigned to each cell. Porosity data were upscaled using an arithmetic method, so the porosity value for each cell results from an arithmetic mean on the relevant depth interval. Permeability was upscaled using a geometric method applied on permeability NMR well-logs issued from GCAH2, GBD1 and GBD3 wells, but also on core plug permeability data. Facies distribution was then propagated to the whole grid by using the “Truncated Gaussian with trend” algorithm. Porosity and permeability were further propagated with the “Gaussian Random Function Simulation” algorithm, using a normal porosity distribution estimated for each single facies according to raw data, and with log-normal distributions for permeability data. Eventually, the model is composed of almost $14.10^6$ cells with the X and Y cell-size set to 100 m.

# 4. RESULTS

## Sedimentary facies

From core and cutting descriptions, detailed sedimentological observations have led to define ten facies covering a wide range of sedimentary textures and granulometries, such as coarse-grained limestones to fine and mud-supported limestones with variable grain size sorting.

Such textures are indicative of a wide range of depositional processes that can be present on a downdip carbonate ramp profile from offshore to protected lagoonal environments. Therefore, identified facies were grouped into four facies associations, from FA1 to FA4, representative of four downdip carbonate ramp depositional environments from the most distal (lower offshore) to the most proximal (lagoon environment): (1) lower offshore referring to facies deposited below the storm wave base (Clays and marls, facies association FA1), (2) upper offshore standing for facies deposited between the storm wave base and the fair-weather wave base (mud-dominated facies, facies association FA2), (3) shoreface or shoal/barrier environments associated to wave- and tide-dominated facies deposited above the fair-weather wave base (ooid dominated facies, facies association FA3), and (4) lagoonal to intertidal environments for both mud-supported and grain-supported limestones deposited in shallow environments and fluctuated energy (peloid dominated facies, facies association FA4).

## Microfacies and NMR signatures from GBD4 cores

Experimental NMR $T_2$ signatures were plotted according to facies definition to reveal the general characteristics of pore-size distributions for each facies (Fig. 2). Note that only four facies were described from core observations.

$F2a$ and $F2b$ facies (grouped in the FA2 upper offshore association) are characterized by $T_2$ distributions displaying a dominant mode at 50 ms illustrating prevailing microporous networks mainly developed within muddy and micritic matrices. Such facies exhibit poor reservoir potential.

$F3b$ and $F3c$ facies (grouped in the FA3 shoreface/shoal association) are characterized by quite similar bimodal $T_2$ distributions showing high amplitudes for $T_2$ times greater than 200 ms demonstrating a quite very well-developed macroporous network. The $F3c$ ooid-dominated facies is very characteristic of the Oolithe blanche formation displaying the best reservoir potential.

These results confirm a correlation between characteristic NMR $T_2$ responses and observed facies types. This highlights that NMR $T_2$ interpretation cannot be achieved properly without considering any geological feature leading to a more factual and accurate identification of reservoir heterogeneities.

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Characteristic NMR $T_2$ pore-size distributions for each facies observed on GBD4 cores.</figcaption></figure>

# 5. DISCUSSION

Both peloid-dominated (Comblanchian formation) and ooid-dominated facies (Oolithe blanche formation) are characterized mainly by intergranular macroporosity showing rare blocky calcite cements and occasionally early cements coating grains that reduced compaction during burial and allowed the preservation of well-developed pore networks. Such rocks are the best targets for geothermal exploitation due to their high permeability.

Projecting in 3D the stratigraphic surfaces and facies allows to build a 3D static geological model conceptualizing a high-resolution reservoir geometry (Fig.3). As shown by the 3D facies model and relevant with the Bathonian paleo-geographical map at the top
of the reservoir, the peloid-dominated facies related to the “Comblanchian” formation progressively disappears westward as attested by the progressive transition from muddy lagoon to ooid shoal.

Lateral continuity of reservoir layers (i.e non or poorly cemented ooid-dominated facies and peloid-dominated facies) can be difficult to predict. Such prediction can be completed using the “Object modelling” algorithm, which is adapted to model localized porous lenses within the reservoir. This step is crucial to produce space granular body geometries in accordance with real geological and diagenetic features.

Each facies of the 3D geological model are characterized by a range of porosity and permeability values providing guidance to select best drilling targets. As the “Gaussian random function simulation” algorithm leads to one probabilistic realisation, numerous porosity and permeability models need to be generated to provide uncertainties estimates assessments on the relevance of modelled petrophysical distributions.

A quality control can be applied to permeability results using transmissivity data interpreted from well tests for all geothermal wells in order to validate the individual permeability prediction in each cell.

<figure><img src="image_5.png" /></figure>

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<caption>**Figure 3:** View of the entire 3D geological model built on Petrel® software.</caption>

## 6. CONCLUSIONS

Such models can be used to assess porosity and permeability space heterogeneities, with the aim to better predict future development areas in the Paris region within the long production and possibly already nearing overexploitation of the Middle Jurassic carbonate reservoir. The present study has emphasized the utmost importance for benefitting from NMR logs in geothermal wells, or core samples to improve estimates of permeability and porosity heterogeneities.

## PERSPECTIVES

Once models have been finalized, a local extraction can be completed to perform thermal and fluid flow simulation in target area. The validity of the models is then evaluated by correlating production results with raw data recorded during geothermal exploitation. The objective is to better assess the impact of the reservoir heterogeneity on fluid flow and therefore de-risk future operations by reliable predicting geothermal breakthroughs.

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