How to cite: Medina, E., Levresse, G., Cortes-Prado, C. A., Cerca, M., Carrera-Hernandez, J., & Mougel, B. (2026). Determination of the effective storage of CO2 at Coyula seismic block level in the Tampico-Misantla province, 43(2), 88–101. DOI: https://dx.doi.org/10.22201/igc.20072902e.2026.2.1900

Revista Mexicana de Ciencias Geológicas, v. 43, num. 2, August 2026, 88–101

DOI: https://dx.doi.org/10.22201/igc.20072902e.2026.2.1900

Determination of the effective storage of CO2 at Coyula seismic block level in the Tampico-Misantla province

Erik Medina1, a, Gilles Levresse2, b,*, Cesar A. Cortes-Prado1, c, Mariano Cerca2, d, Jaime Carrera-Hernandez2, e, and Berengere Mougel2, f

1 Posgrado en Ciencias de la Tierra, Universidad Nacional Autónoma de México, UNAM Campus Juriquilla Blvd. Juriquilla 3001, Querétaro, 76230, Mexico.

2 Instituto de Geociencias, Universidad Nacional Autónoma de México, UNAM Campus Juriquilla Blvd. Juriquilla 3001, Querétaro 76230, Mexico.

*Corresponding author (G. Levresse): glevresse@geociencias.unam.mx

a 0000-0002-8703-2747; b 0000-0001-9290-9825; c 0000-0002-7916-5398; d 0000-0002-9264-4553; e 0000-0002-4418-9277; f 0000-0002-9226-7700

 

ABSTRACT

Carbon capture and storage methods are commonly accepted as an essencial way to mitigate atmospheric CO2 concentrations. Despite being a significant petroleum producer, Mexico is committed to combating climate change encouraging the use of carbon capture and storage and enhance oil recovery technology. In this research, the storage capacity of CO2 in the reservoir of a deep saline aquifer located in the Coyula seismic block in the Tampico Misantla Basin, is estimated using the volumetric approach where parameters like CO2 density, and porosity of the geological formation, and efficiency storage factor are considered. To achieve a better evaluation of the site and to constrain the effective storage potential of the Coyula seismic block, both open and close systems were tested. A conservative evaluation for all the identified reservoirs, delimited in the Cahuasas Jurassic formation within the Coyula seismic block, amounts to 25.6 Mton CO2. Compared to the emissions from regional sources (8 Mton of CO2 per year), the minimum geological reservoir evaluation encourages further detailed studies to improve the estimation of reservoir volumes and CO2 storage feasibility.

Keywords: Screening and evaluation of CO2 storage; saline aquifer; CO2 storage; regional characterization; Mexico

 

RESUMEN

Los métodos de captura y almacenamiento de carbono son ampliamente aceptados como una forma esencial de mitigación de las concentraciones atmosféricas de CO₂. A pesar de ser un importante productor de petróleo, México está comprometido con la lucha contra el cambio climático, promoviendo el uso de tecnologías de captura y almacenamiento de carbono, así como de recuperación mejorada de petróleo. En esta investigación, se estima la capacidad de almacenamiento de CO₂ en el reservorio de un acuífero salino profundo ubicado en el bloque sísmico Coyula, en la Cuenca Tampico-Misantla, utilizando el enfoque volumétrico, en el cual se consideran parámetros como la densidad del CO₂, la porosidad de la formación geológica y el factor de eficiencia de almacenamiento. Para lograr una mejor evaluación del sitio y delimitar el potencial efectivo de almacenamiento del bloque sísmico Coyula, se analizaron tanto sistemas abiertos como cerrados. Una evaluación conservadora de todos los reservorios identificados, delimitados en la formación jurásica Cahuasas dentro del bloque sísmico Coyula, arroja una capacidad de almacenamiento de 25.6 millones de toneladas de CO₂. En comparación con las emisiones de las fuentes regionales (8 millones de toneladas de CO₂ por año), la evaluación mínima del reservorio geológico fomenta la realización de estudios más detallados para mejorar la estimación de los volúmenes del reservorio y la viabilidad del almacenamiento de CO₂.

Palabras clave: selección y evaluación del almacenamiento de CO₂; acuífero salino; almacenamiento de CO₂; caracterización regional; México

 

 

INTRODUCTION

The increasing concentration of CO2 in the atmosphere due to the development of industrial activity during the last century has significantly impacted the global climate, the ecological environment and is currently jeopardizing our way of life. Concerned for the consequences of carbon concentrations, societies have called for a global effort to limit the CO2 emissions or to reverse the increasing trend of CO2 concentration in our atmosphere. To reduce the amount of CO2 in the atmosphere, Carbon Capture and Storage (CCS) methodologies have been recommended as a viable option (Torp and Gale, 2003).

Accordingly, CCS methods have been continuously developed since the 1990s. One of the most promising approaches is CO2 storage in saline aquifers, because it offers a high-potential carbon storage under CO2 supercritical conditions (31.1 °C and 7.38 MPa), and CO2 can be retained at depth through several processes, such as structural/stratigraphic trapping, residual trapping (capillarity force in porosity at irreducible gas saturation), solubility trapping (dissolved into formation water), as well as mineral trapping (incorporated in newly formed minerals during water/rock interaction). As any geological injection processes, injection of CO2 into deep saline aquifers is not an easy task. It requires a multicomponent, multiphase flow system, in which geomechanics, geochemistry, and thermal effects may be important (Al-Khoury and Bundschuh, 2014).

The capacity of CO2 storage in geological formation is not consequently a straightforward or simple process. Luo et al. (2022) have recently reviewed the different storage mechanism applied worldwide and pointed out the challenges of evaluating the different stages involved. The CO2 storage capacity estimation methods are divided in static and dynamic methodologies. Static methods involve the characterization of the physical properties of the aquifer, such as porosity and permeability, to calculate the total volume. The dynamic methods involve simulating the fluid flow and transport processes in the aquifer to estimate the effective storage potential, and the CO2 behavior over the time (Bachu, 2003). The dynamic methods require defining more data to characterize the thermal mechanic and hydrologic processes. It also requires more precise boundary conditions of the potential reservoir rock and fluids. In close systems, the reservoir volume is restricted by the impermeable geological boundary (structural or stratigraphic), which prevent fluid movement, and volume variation. Conversely, open systems permit vertical and/or lateral fluid movements through permeable boundaries, thereby increasing porosity availability and enabling volume variations.

This study evaluates the CO₂ storage potential of Jurassic saline aquifers in the Tampico-Misantla Basin, focusing on Coyula seismic block scale as a last step before dynamic, site-specific evaluations. The main objective is to estimate effective storage capacity using locally derived petrophysical data, aiming to reduce uncertainty associated with global averages commonly used in previous studies, such as those by the IEA-GHG (2009). A lithostratigraphic-structural model was developed, identifying three potential storage sites within the basin. Static storage capacity was calculated using effective storage coefficients tailored to local geological conditions. This approach addresses challenges related to limited site-specific data, which currently prevent detailed dynamic modeling of CO₂ flow and transport. The study builds on prior regional assessments that highlighted the suitability of the basin for carbon capture and storage, offering refined estimates that can support future CCS planning and implementation in northeastern Mexico.

 

GEOLOGICAL FRAMEWORK

The central part of the Mexican continental margin of the Gulf of Mexico is composed of the Tampico Misantla basin (TMB; Figure 1) which consists of a low-lying (0 to 500 m a.s.l) mountainous plain. Its northern part is bordered by the Sierra de Tamaulipas, and to the south is the Trans Mexican Volcanic Belt. On the western and eastern sides, the TMB is bounded by the Gulf of Mexico, and the Sierra Madre Oriental, respectively.

 

 

The Sierra Madre Oriental belongs to the Mexican Fold and Thrust Belt tectonic unit (MFTB; Figure 1; Fitz-Díaz et al., 2018). Its formation is related to the Laramide Orogeny and the associated subsidence of former carbonate platforms together with the development of Late Cretaceous–Paleogene flexural Mexican Foreland Basins (MFB, Figure 1; Suter, 1980; Roure et al., 2009; Lawton et al., 2016) including the Burgos (BB), the Tampico-Misantla (TMB), and the Veracruz Basin (VB). The MFB forms a large and low-altitude terrain in direct contact with the MFTB (Eguiluz de Antuñano, 2011). Overall, the TMB itself exhibits a complex geological record, where Mesozoic sediments overlie the Precambrian and Paleozoic basement, which in turn underlies Triassic units of volcanic and sedimentary origin (Carrillo-Bravo, 1971; Fitz-Díaz et al., 2018).

Figure 1 presents a geological map detailing the lithology of geological units from the region, as well as and the main structures which show evidence for tectonic shortening and extension. The sedimentary and structural contacts between units from the Jurassic to more recent periods are illustrated in a redrawn regional geological cross-section from the Comisión Nacional de Hidrocarburos (2018; A-A’ profile in Figure 1). It is worth pointing out that the study area has been affected by the pre- and syn- rifting and drifting episodes of the Gulf of Mexico (GOM) during the Jurassic, which led to the reworking of the continental rocks and syn-rift sediments formed before the Jurassic (Coombs et al., 2020). The subsequent rotational opening of the GOM coupled with the translation of the Yucatan Block enabled the formation of north-south regional shear zones across eastern Mexico (Pindell et al., 2020; Coombs et al., 2020). These zones accommodated substantial displacements, fragmenting paleogeographic elements like the massif of Chiapas and that of Tampico (Pindell et al., 2020; Coombs et al., 2020) and ultimately followed by the development of the extensional TMB (Martini and Ortega, 2018). Reported evidence for post-rift facies and carbonate platform deposition (Hernández-Romano et al., 1997) suggest that a passive margin environment was prevailing throughout much of the Cretaceous. However, during the Paleocene-Eocene, the deformation characterized by significant shortening led to the formation of the Chicontepec foreland sub-Basin, being related to the Mexican Orogenic event (Fitz-Díaz et al., 2018). This basin is composed of siliciclastic sediments deposited during the orogenic period (Eguiluz de Antuñano, 2011; Fitz-Díaz et al., 2018). Consequently, formations formed prior and during the Mesozoic experienced deformation and structuring within the fold-thrust belt, with the migration of the shortening front towards the east and overriding units of Middle Cretaceous age onto sedimentary deposits from the Eocene (Figure 1). The oldest formations found within the TMB are represented by the Huayacocotla and Cahuasas Formations, dating back to the Toarcian and Callovian ages, respectively (Figure 1). The Huayacocotla Formation, is mainly composed of quartz-rich conglomerates and sandstones, originated in a continental basin (Esquivel-Macías et al., 2017). In contrast, the Cahuasas Formation is composed of conglomerates, sandstones, siltstones, and shales reflecting a fluvial and alluvial depositional environment (López-Infanzón, 1986). This continental phase ended-up with the deposition during the Middle Jurassic of, wackestone, anhydrite mudstone, and anhydrite, composing the Huehuetepec Formation (Nava-Pérez & Alegría-Luna, 2001). Subsequently, the Upper Jurassic units known as the Pimienta, Tamán, and Tepexic-Santiago formations show radical changes in composition with the presence of limestones, carbonaceous mudstones, and shales rich in organic matter. Concurrently, oolitic limestones, likely deposited at the basin margin, can be found in the San Andrés Formation (Todd, 1976). Predominant Sedimentary facies recorded during the Cretaceous are that of basin and oceanic shelf, with carbonate reefs developing on paleogeographic summits (López-Doncel, 2003; Rodríguez–Hernández, 2007). This entire period is characterized by gentle and prolonged subsidence. The lower part of the Tamaulipas Formation marks a transition phase during the Early Cretaceous, beginning with grainstones (oyster shape) and packstones interspersed with layers composed of bentonite and shale, and gradually transitioning to thicker limestone units upward (Hernández-Romano et al., 1997). Maximum estimated subsidence depth occurred during the Albian-Cenomanian, with oceanic basin deposition facies. The upper part of the Tamaulipas Formation comprises limestones and shales of argillaceous and calcareous compositions, respectively. Early Cretaceous is represented by Santuario, Tamabra, El Abra, and Tamaulipas calcareous and carbonaceous formations. While the Santuario Formation mainly consists of limestones (dark-gray color), calcarenites, greywackes, and phyllitic shales (Segerstrom, 1962), the other formations are primarily composed of limestones. Notably, the Tamabra Formation includes interbedded calcareous breccia, the el Abra Formation features thick-bedded limestone, and the Tamaulipas Formation exhibits limestones with chert lenses and some evaporites at its bottom (Guaxcama Fm.). The Agua Nueva Formation (Early Turonian) is characterized by limestones enriched in clay and carbonates as well as calcareous shales. Formations known as San Felipe and Méndez are also very similar to Agua Nueva although they exhibit bentonite intercalations (Seibertz, 1986). During the Paleogene, the dominant sedimentary facies became siliciclastic, comprising a thick sequence of landslides, debris flows, and turbiditic flows that filled foreland basin of the Sierra Madre Oriental. These units are called Velasco, Chicontepec, and Guayabal formations (after Barker and Berggren, 1977). This period is also represented by the formation of the Bejuco-La Laja paleo-canyon and the Chicontepec paleo-channel (Santillán-Piña & Aguayo-Camargo, 2011). The upper portion of the overall detritic sequence, spanning from the Oligocene to the Recent, is composed of interbedded sandstones and shales, illustrated by Tantoyuca, Chapopote, Horcones, Palma Real, Mesón, and Tuxpan formations (Guzmán-Arellano, 2012).

The sedimentary column of the TMB also shows some magmatic intrusions of Middle Eocene to Quaternary ages associated with the Eastern Mexican Alkaline Province (EMAP). In the northern part of the TMB, as well as in Tamaulipas highs, these intrusions primarily consist in porphyritic and equigranular bodies, with minor hypabyssal igneous bodies, dating between approximately 44 and 20 million years ago (Hamblock, 2002; Pindell, 2009; Zhou -Décida et al., 2009). Later (< 16 million years ago) prevalent lava flows and dikes have been reported within the basin (Ferrari et al., 2005). East of the TMB lies the Aldama Volcanic Field, composed of alkali basalts and derived olivine trachytes from the Pliocene to the Pleistocene exhibiting geochemical signatures typical of intraplate magmatism (Camacho-Angulo, 1993; Aranda-Gómez et al., 2005). Finally, the Llera de Canales Volcanic Field, located west of the Sierra de Tamaulipas and contemporaneous with the Aldama Volcanic Field, reports some basanitic lava flows, some alkaline basalts, and sodic trachybasalts (i.e. hawaiites) lava flows (Aranda-Gómez et al., 2005 and references therein). Viera-Décida et al. (2009) also suggested that the igneous rocks from Palma Sola and from Los Tuxtlas that have been emplaced during the Late Upper Oligocene to Miocene, at the southern border of the TMB, may represent the southernmost extent of the EMAP.

 

RESERVOIR CHARACTERIZATION

Comisión Nacional de Hidrocarburos (CNH, 2018) data from oil wells penetrating the Jurassic column were used to characterize the petrophysical properties of the target saline aquifers. Regional 3D seismic reflection surveys are combined with detailed well-log analyses from Pemex-PEP exploration wells. Lithology mudlogs, which describe the main rock types within specific depth intervals, are constructed using core descriptions. These descriptions encompass key features such as grain size, mineralogy, grain distribution, matrix composition, density, and fluid content.

 

Wells selection

Five wells—Planos-1, Palmar-1, Tablon-1, Defense-1, and Tecomate-1—met the primary selection criteria: availability of geological and petrophysical data, and penetration into the Jurassic sedimentary sequence within the seismic survey area (Figures 2 and 3). These wells encountered the reservoir Jurassic formations at depths ranging from 3200 to 4900 meters below the surface. Penetration depth into the Jurassic formations varied among the wells. All five wells intersected the uppermost 500 to 600 meters of the Jurassic column. The Cahuasas Formation, characterized by sandstone lithology, constitutes the top of the Jurassic sequence. This formation maintains a relatively consistent thickness of approximately 400 meters across the Coyula 3D seismic block. Overlying the Cahuasas sandstone is the Huehuetepec Formation, composed of Upper Jurassic shale. Notably, the base of the Huehuetepec Formation contains discontinuous evaporite layers. These evaporite layers serve as both a reliable marker horizon for stratigraphic and petrophysical correlation and an effective caprock due to their distinct geological and petrophysical properties.

 

 

 

The seismic model and reservoir volume estimation

A lithostratigraphic-structural model of the study area was constructed using Petrel® software (version 2013). The 3D seismic reflection cube was calibrated using data from the five positioned well logs. Regional interpretation of the seismic data, with a 25-meter profile spacing, made it possible to identified key stratigraphic horizons and faults. Following digitization and interpretation (2D), the faults and stratigraphic tops were converted into surfaces, forming the basis of the 3D stratigraphic-structural model. Linear interpolation was employed for this conversion. To estimate reservoir volume, a 3D geocellular model was built using the interpreted structural and stratigraphic data from the depth-converted 3D seismic cube. Geological contacts defined using well log data, lithological descriptions, and paleontological reports, guided the interpretation of surfaces and faults. This interpretation informed the creation of a structural model with sub-horizons and faults, which was then used to generate a 3D orthogonal cell grid comprising 225600 cells and 237456 nodes. Petrophysical properties, including porosity and permeability, were extrapolated through the 3D cell grid using geostatistical methods constrained by interval velocity and validated with core measurements. Finally, a geometric volume was calculated for each cell, and an index filter was applied to delineate the prospective reservoirs

 

Petrophysical properties

Porosity and permeability

Porosity and permeability were initially determined from well-log measurements and then validated against core sample analyses from Tecomate-1 well to ensure accurate representation of these well-log reservoir properties (Figure 4). Two methods, Wyllie Time Average (sonic porosity) and Neutron porosity, were employed to estimate porosity. Permeability was determined using the Wyllie-Rose method for sands and the Coates and Dumanoir method for fine-grained facies (Wyllie and Rose, 1950; Coates and Dumanoir, 1974). Notably, the sonic porosity and Wyllie-Rose permeability estimations demonstrated good agreement with helium porosity and steady-state permeability measurements conducted on core samples from the Tecomate-1 well. These core samples, extracted from calcareous sandstone facies at the top of the Cahuasas Formation, provided valuable ground truth data. Furthermore, a lithological index, integrating porosity and permeability information, was interpreted to classify the predominant facies within the Tamán, Tepexic, Santiago, and Cahuasas formations, enhancing the understanding of reservoir heterogeneity (Figure 5).

 

 

Vertical stress and pore pressure profiles

The vertical stress profile was determined using a combination of bulk density logs and extrapolated density estimations for shallow intervals where log data might be limited. Vertical pore pressure profiles were estimated using the Eaton method, which utilizes compressional slowness logs (Eaton, 1972). These estimations were then validated against mud density profiles and gas reports for accuracy. Mud-loss pressure profiles were estimated using the Matthews and Kelly (1967) method, assuming a ratio of 0.75 between the vertical stress and the minor horizontal stress. This assumption is often used in regions with limited stress data. These estimations were further validated against mud-loss and ballooning events documented in drilling reports from five offset wells, providing valuable insights into actual wellbore conditions. The use of offset well data enhances the reliability of the mud-loss pressure profile.

 

Dynamic elastic modulus

The dynamic elastic modulus was determined using density and sonic log data (compressional and shear). Where density log data were unavailable, a correlation between compressional sonic and density logs, tailored to the specific lithological facies, was employed. The shear sonic log, a key component in calculating the dynamic elastic modulus, was estimated using Castagna et al. (1985) relations for saturated sandstones and shales. This approach leverages the relationship between compressional and shear wave velocities in different lithologies. It is important to note that the dynamic elastic modulus, estimated from acoustic wave propagation in an isotropic elastic medium, is typically higher than the static elastic modulus. This difference arises because dynamic measurements, acquired under dynamic conditions, involve smaller deformation amplitudes compared to static measurements.

 

Effective storage capacity estimation

Structural highs, representing structural traps, were identified as potential CO2 storage sites. This selection was based on the positive buoyancy of CO2, which tends to migrate upwards, and the presence of coarse-grained alluvial fan deposits (sandstones) within these structures, providing suitable reservoir characteristics. However, due to limited data on the hydraulic properties (specifically, transmissivity) of the faults bounding these potential reservoirs, two boundary condition scenarios were considered, open and closed systems. A closed system assumes that faults behave as sealing boundaries, effectively compartmentalizing the reservoir and restricting fluid flow to the defined structural closure. In contrast, an open system allows for hydraulic connectivity with adjacent stratigraphic or structural units, enabling pressure dissipation and fluid exchange across boundaries. Evaluation of these end-member scenarios constrains the envelope of possible system behaviors and supports a more rigorous quantification of volumetric capacity and dynamic uncertainty. The closed system represents the most conservative, capacity-limited scenario.

 

Open system

To estimate the effective storage capacity of the identified geological structures as open systems, we employed the DOE methodology with a probabilistic approach. This approach utilizes effectiveness coefficients representing the P10, P50, and P90 percentiles for parameters influencing storage capacity (U.S. DOE, 2007). The total CO2 storage capacity (GCO2T) is calculated using the following equation:

GCO2T = A x h x Φ x ρCO2 x EE (1)

where:

A: reservoir area (given in m2, obtained from seismic and petrophysical data (Figures 5 and 6)).

h: reservoir thickness (given in m, obtained from seismic and petrophysical data (Figures 5 and 6)).

Φ: porosity (given in m3/m3, calculated from well logs and laboratory analyses (Figure 6)).

ρCO2: CO2 density (given in kg/m3, estimated based on reservoir depth and temperature).

EE: efficiency coefficient (calculated using Equation 2).

 

 

To estimate realistic values for the storage capacity of the specific reservoirs (A, B, C, Figure 5), we decide to use the most conservative characteristic values for the different horizon in the considered properties.

The efficiency coefficient (EE) represents the combined effect of geological, volumetric, and microscopic displacement efficiencies (IEA-GHG, 2009):

EE = EGeol x EV x Ed (2)

EGeol is the geological efficiency (dimensionless). This factor accounts for the portion of the geological unit suitable for CO2 storage, considering net area, net thickness, and effective porosity:

EGeol = (An/At) x (hn/hg) * (Φef/ΦT) (3)

where:

An (Net area): expressed in square meters (m²).

At (Total area): expressed in square meters (m²).

An/At : net-to-gross area ratio (dimensionless), determined from petrophysical models using a minimum total porosity cut-off of ΦT = 0.10; (Hatzignatiou et al., 2011).

hn/hg: net-to-gross thickness ratio (dimensionless), derived from well logs using a clay volume cut-off of 0.20; thickness values are expressed in meters (m; Figures 5 and 6).

Φef (Effective porosity): expressed as a fraction or percentage (v/v). It represents the interconnected pore space available for fluid flow and storage (dimensionless).

ΦT (Total porosity): expressed as a fraction or percentage (v/v). It includes both connected and isolated pores, as well as bound water associated with clays (dimensionless).

Φef/ΦT: effective porosity fraction (calculated from petrophysical logs, (Figure 3)), dimensionless (fraction), indicating the proportion of the total pore space that effectively contributes to fluid flow or storage.

Ev (volumetric displacement efficiency) represents the fraction of pore space accessible to CO2 injection. Due to limited data on reservoir heterogeneity, Ev values were derived from the IEA-GHG (2009) report (Appendix E corresponding to sandstones of an alluvial fan depositional environment).

Ed (efficiency of microscopic displacement) represents the fraction of pore space where CO2 can effectively displace the existing fluids (e.g., water). It is calculated using the following equation (Bassiouni, 1994):

Ed = 1-Swirr

and: (4)

where:

Swirr: irreducible water saturation (the fraction of pore space occupied by immobile water, dimensionless).

Rw: water resistivity (given in Ohmmeter, obtained from well logs).

ΦT: total porosity (obtained from well logs, dimensionless).

Rt: deep penetration resistivity (given in Ohmmeter, obtained from well logs).

a, m, n: Empirical constants (dimensionless).

For this study, the following values were used based on the lithology (carbonate rocks: a = 1, m = 2, n = 2; silicoclastic rocks: a = 0.62, m = 2.15, n=2 (Table 1; Archie, 1942).

These parameters, along with the well log data, allow for the calculation of Ed, which contributes to the overall efficiency coefficient for estimating CO2 storage capacity (Table 2).

 

 

 

Close systems

For closed systems, where CO2 migration is restricted, the storage capacity calculation considers the compressibility of the rock and fluids. The equation for total CO2 storage capacity in a closed

system is:

GCO2T = A x h x Φ x ρCO2 x Ecomp (5)

where:

Φ: porosity (given in m3/m3, calculated from well logs and laboratory analyses, Figure 6).

ρCO2: CO2 density (given in kg/m3, estimated based on reservoir depth and temperature).

Ecomp: compressibility effectiveness coefficient (given in MPa, calculated using Equation 6).

This coefficient accounts for the volumetric changes due to the compressibility of rock and water under increased pressure from CO2 injection. It is calculated as follows (Zhou et al., 2008; U.S. DOE, 2007):

Ecomp = (βr + βa)Δp (6)

where:

βr: rock compressibility (calculated as 1/K, where K is the compressibility modulus obtained from the petrophysical model).

βa: water compressibility (assumed constant at 4.6e-10 Pa-1).

Δp: (Pp- Pf) pressure increase, where Pp is pore pressure, Pf is the pressure of fracturation (using the synthetic curves of effective porosity and DTCO measurements for each horizon of interest in the reservoirs (Eaton, 1975; Bowers 1995)).

The rock compressibility is determined by the relationship βr  1/K. The compressibility modulus K was obtained in each of the horizons of interest from the petrophysics model (Zimmerman, 1991; Ahmed, 2006; Table 1). The water compressibility (βa) is considered a constant (4.6 e-10 Pa-1; EIA-GHG, 2009).

 

DETERMINATION OF EFFECTIVE STORAGE CAPACITY IN SELECTED AREA

The CO2 storage capacity for the Cahuasas Upper Jurassic formation within the Coyula seismic block was calculated based on the lithology and the petrophysical properties of the five exploratory oil wells crossing the seismic block. Three main reservoirs were identified (A, B, C in Figure 5). All the reservoirs are sealed by a thick efficient caprock composed of shale and evaporite characterized for their very low permeability (Figure 4). The trapping mechanism involves stratigraphic trapping through lateral delta and alluvial fan facies variations. Figures 5 shows the reservoirs A, B, C depth range from 3600 to 3900 m. The three reservoirs present the same lithology made off sandstone with shaly layers intercalation. For accuracy, the CO2 density is estimated as a density range, following the reservoir depth and thickness and reservoir temperature variation (Medina et al., 2023). The full data set use to develop the Equation 1 for open system and Equation 5 for close system are summarized in Table 1. We estimated the CO2 potential storage resources for each Cahuasas stratigraphic layers of the three reservoirs (A, B, C). The range of the petrophysics characteristics are comparable in all reservoirs. The pore and fracture pressures vary from 36.9 to 46.5 MPa and from 72.8 to 90.8 MPa respectively. The reservoir A shows wider porosity and relative homogeneous permeability varying from 0.07 to 0.2 % and 0.26 to 2.5 mD, compared to B and C reservoirs, ranging from 0.12 to 0.18 % and from 0.2 to 6.5 mD. Irreducible water values varied between 47 to 60 %. The density of CO2 is estimated as a min and max range from 417 to 663 kg/m3, considering the reservoir temperature and lithologic pressure. The reservoir volumes are underestimated and still comparable (Table 2). The volumes of reservoirs A and B are constrained by the lateral extent of the seismic blocks rather than by stratigraphic closure (Figure 5).

For the determination of the effective storage coefficients in a closed system hypothesis, the porosity is presented as a weighted mean by reservoirs. The efficiency coefficient (EE) estimate for each reservoir is comparable ranging from 1.79 (reservoir C) to 2.03 (reservoir A). The effective storage capacities of CO2 are estimated as a minimum and maximum range. The results are presented in volumetric and mass effective storage capacity (Table 3). The ranges vary from 3.83 to 6.09 Mton for reservoir A, from 4.93 to 7.83 Mton for reservoir B, and from 7. 63 to 11.70 Mton for reservoir C, for a total at the Coyula seismic block ranging from 16.11 to 25.62 Mton CO2. The most impacting data in the final CO2 mass estimation is the reservoir volume.

 

 

For the open system hypothesis, we used the same petrophysical data than previously. We estimate efficient coefficients for open system for a sedimentary environment of sandstone deposited in an alluvial fan with a monoclinal structure, shown in the Table 4 for the 10, 50, and 90 percentiles (P; Tables 1 and 4).

The results are presented in volumetric and mass effective storage capacity. Geological and petrophysical data vary in comparable way in the three reservoirs. Reservoir volume and the associated percentiles represent the key parameters that exert the greatest influence on the final storage potential estimation (Table 4). The maximum and minimum effective storage capacity obtained for the reservoirs in the Coyula block range from 11.21 to 17.28 Mton (P10) to 27.32 to 43.44 Mton (P90) for reservoir A, from 3.8 to 6.16 Mton (P10) to 9.69 to 15.41 Mton (P90) for reservoir B, and from 10.56 to 16.79 Mton (P10) to 38.35 to 60.97 Mton (P90) for reservoir C, for a total estimation at Coyula block of 25.65 to 40.78 Mton (P10) to 75.36 to 119.82 Mton (P90). We consider these values a conservative estimate for the potential of the Cahuasas sandstone.

 

 

When compared with capacity estimates from some of the most extensively studied CO₂ storage projects worldwide—such as Quest and Weyburn in Canada, Cranfield in the USA, Sleipner in Norway, and Gorgon and Otway in Australia—the results obtained in this study appear both consistent and promising. These reference sites span a range of geological contexts, including deep saline aquifers (e.g., Sleipner, Quest, Gorgon, Otway) and depleted or producing hydrocarbon reservoirs (e.g., Weyburn, Cranfield), providing a robust benchmark for comparison.

In terms of storage capacity, these projects typically range from several tens to hundreds of millions of tonnes of CO₂. For instance, Sleipner has been injecting approximately 1 million tonnes of CO₂ per year since 1996 into a saline aquifer (Furre et al., 2017), while Gorgon represents one of the largest projects globally, with planned storage on the order of hundreds of millions of tonnes (Weijmars, 2024). Similarly, Quest has stored several million tonnes of CO₂ in a deep saline formation, and Weyburn and Cranfield demonstrate significant storage potential in carbonate and sandstone reservoirs associated with hydrocarbon systems (White, 2011). These sites are characterized by well-constrained structural or stratigraphic traps, adequate reservoir quality (porosity and permeability), and effective sealing formations.

Within this global context, the capacities estimated in the present study fall within a comparable order of magnitude, considering the scale of evaluation and available data. The geological setting, involving sedimentary formations with suitable petrophysical properties and structural configurations, further supports the relevance of the comparison. Although uncertainties remain—particularly related to data density and model resolution—the results suggest that the studied reservoirs share key characteristics with established storage sites.

 

MODEL UNCERTAINIES

Effective capacity is typically estimated using probabilistic volumetric approaches that incorporate storage efficiency coefficients derived from numerical simulations and calibrated with petrophysical data. Such methods allow the integration of geological variability and uncertainty, providing a range of possible storage values rather than a single deterministic estimate. Effective capacity thus represents a more realistic and operationally relevant assessment of storage potential and serves as an intermediate metric between theoretical and practical capacities, particularly useful for site screening and early-stage evaluations (CSLF, 2007; U.S. DOE, 2007; Bachu, 2003; IEA-GHG, 2009).

A major challenge in estimating effective capacity lies in the quantification of uncertainties, especially those related to seismic interpretation and interpolation of reservoir properties. Seismic data define reservoir geometry and structure but are affected by resolution limits, velocity model uncertainties, and interpretation subjectivity. These uncertainties propagate into key volumetric parameters such as thickness and reservoir extent and are further amplified during time-to-depth conversion in heterogeneous formations. Similarly, the interpolation of petrophysical properties between wells introduces additional uncertainty. Given these cumulative uncertainties, deterministic values can be misleading. A probabilistic framework is therefore essential, where uncertainty is propagated through volumetric calculations to generate distributions of storage capacity. The use of statistical percentiles (P10, P50, P90) allows the definition of reliable ranges, with P50 representing the most likely value and P10–P90 bounding optimistic and conservative scenarios. Expressing storage capacity as percentile ranges provides a robust and transparent basis for risk-informed decision-making, particularly in early-stage assessments where data limitations are significant.

 

SUMMARY AND CONCLUSION

The Cahuasas Upper Jurassic formation in the Coyula seismic block shows promising characteristics for CO2 storage, including permeable strata sealed by impermeable layers at depths suitable for injection (3500 – 4000 m). Identified saline aquifers exhibit high permeability (0.2 – 6.05 mD for sandstone and clay intercalations) and porosity ranging from 0.07 % to 0.2 %, making them suitable for CO2 storage. These layers are suitable reservoir rocks qualified for permanent CO2 storage. The CO2 storage potential in the Cahuasas formation at the Coyula block was calculated based on the rock compositions and petrophysical properties from exploratory oil wells. Three stratigraphic reservoirs, called A, B and C were identified sealed by Huehuetepec shale formation. The prospective storage resources of the three reservoirs were calculated locally, integrating seismic profiles and wells data considering the open and close systems hypothesis. In this case the close system will be the most conservative results (16.11 to 25.62 Mton CO2) as a first site evaluation. This study suggests that the CO2 storage capacity ranges approximately for open system from 25.65 to 40.78 Mton (lower range), and 75.36 to 119.82 Mton (maximum conditions). Summing the three Cahuasas formation reservoirs, the geological storage efficiencies range from 0.08 % to 4.3 %. The results obtain from the close system hypothesis are comparable to the lower characteristic ranges (P10) of the open system. The Cahuasas formation within the Coyula block, representing less than 10 % of the Tampico Misantla basin, demonstrates significant CO2 storage potential, exceeding the estimated annual CO2 emissions from the basin's oil industry (approximately 4 Mton; Medina

et al., 2023).

The uncertainty associated with subsurface data gaps was incorporated into the storage resource evaluation due to the legacy of seismic data and the relatively limited well data available over the study area. As well, the upper Jurassic reservoirs show evidence of alluvial fan geometry and natural permeability heterogeneity and/or reactivation structures (Figure 1, Aguayo-Camargo et al., 2018). Jurassic faults and lateral permeability variation are potentially boundaries defining a “semi-closed system” from which the lateral displacement of brine during CO2 injection might be prevented and contained, avoiding a pressure buildup in the reservoir (e.g., Zhou et al., 2008).

Overall, the comparison with reference sites indicates that the storage potential identified is credible and sufficiently robust to justify advancing to the next stage of detailed evaluation. This should include refined geological and dynamic modeling, improved constraint of petrophysical parameters, and a more comprehensive uncertainty assessment, in order to better define injectivity, containment, and long-term storage performance.

 

Acknowledgments. This research is part of the first author’s PhD project in the framework of the Universidad Nacional Autónoma de México (UNAM) Postgraduate Program. The research was funded by SENER-CONACYT grant CB-263486 to G. Levresse. We are grateful to the Comision Nacional de Hidrocarburos, and the Secretaria de Energia for the technical support and data access through the ARES-NVR-MX-16-4Y8 agreement.

Author contributions. Erik Medina: Methodology, investigation, data processing, analysis and interpretation, writing, Gilles Levresse: Methodology, investigation, data processing, analysis and interpretation, writing,, Cesar A. Cortes-Prado: data processing, analysis and interpretation, review, Mariano Cerca: Methodology, formal analysis, review, Jaime Carrera-Hernandez: Review and investigation, Berengere Mougel: Review and investigation. All authors discussed the results and contributed to the writing of the manuscript.

Data availability statement. The authors declare that the data supporting the findings of this study are available upon request from GL. The data are not publicly available due to confidential agreement with CNH.

Declaration of competing interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding: CONACYT grant CB-263486.

 

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Editors:

Luca Ferrari

Luigi A. Solari

 

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