porosity

Petrophysical analysis of downhole logs requires accurate knowledge of matrix properties, commonly referred to as matrix adjustments. In organic-rich shale, the presence of abundant kerogen (solid and insoluble sedimentary organic matter) has a disproportionate impact on matrix properties because kerogen is compositionally distinct from all inorganic minerals that comprise the remainder of the solid matrix. As a consequence, matrix properties can be highly sensitive to kerogen properties. Moreover, the response of many downhole logs to kerogen is similar to their response to fluids. Relevant kerogen properties must be accurately known to separate tool responses to kerogen (in the matrix volume) and fluids (in the pore volume), to arrive at accurate volumetric interpretations. Unfortunately, relevant petrophysical properties of kerogen are poorly known in general and nearly always unknown in the formation of interest.

A robust method of “thermal maturity-adjusted log interpretation” replaces these unknown or assumed kerogen properties with a consistent set of relevant properties specifically optimized for the organic shale of interest, derived from only a single estimate of thermal maturity of the kerogen. The method is founded on the study of more than 50 kerogens spanning eight major oil- and gas-producing sedimentary basins, 300 Ma of depositional age, and thermal maturity from immature to dry gas (vitrinite reflectance, R_o, ranges from 0.5 to 4%). The determined kerogen properties include measured chemical (C, H, N, S, O) composition and skeletal (grain) density, as well as computed nuclear properties of apparent log density, hydrogen index, thermal- and epithermal-neutron porosities, macroscopic thermal-neutron capture cross section, macroscopic fast-neutron elastic scattering cross section, and photoelectric factor. For kerogens relevant to the petroleum industry (i.e., type II kerogen with thermal maturity ranging from early oil to dry gas), it is demonstrated that petrophysical properties are controlled mainly by thermal maturity, with no observable differences between sedimentary basins. As a result, universal curves are established relating kerogen properties to thermal maturity of the kerogen, and the curves apply equally well in all studied shale plays. Sensitivity calculations and field examples demonstrate the importance of using a consistent set of accurate kerogen properties in downhole log analysis. Thermal maturity-adjusted log interpretation provides a robust estimate of these properties, enabling more accurate and confident interpretation of porosity, saturation, and hydrocarbon in place in organic-rich shales.

The solid skeleton of the mudcake consists of fine-grain particles; therefore, a mudcake plug is expected to have a very low permeability and a very good ability to isolate the fracture from wellbore pressure. This requires a relatively permeable formation for two reasons: Mudcake buildup requires fluid loss into the formation, and fracture pressure needs to dissipate after being isolated from the wellbore (Kumar et al. 2010).

Determining the potential of shale-gas reservoirs involves an exhaustive process of calculating the volume of total gas, or original gas in place (OGIP). The calculation of total gas relies on calibrating wireline logs to core data, which are considered to be an empirical validation or ‘ground truth’. However, inconsistency in sample preparation and analytical techniques within, and between laboratories creates significant uncertainty in calculating the free- and adsorbed-gas components, which constitute total gas. Here, we present an analytical program performed on samples of core to elucidate the causes of uncertainty in calculation of total gas. The findings of this program are used to propose improved methods of calculating total gas from core.

Free gas calculated from properties, such as porosity and water saturation measured on core, was found to be highly dependent on laboratory analytical protocols. Differences in sample preparation and water extraction methods led to relative differences of 20% in water saturation and 10% in porosity observed between laboratories, leading to differences of 35% in calculations of free gas in place (FGIP).

Adsorbed gas was evaluated using methane adsorption testing to study the changes in Langmuir parameters in samples with a wide variety of water saturations, clay content, and total organic content over a range of temperatures. It was found that the storage capacity of adsorbed gas artificially increased by a factor of two to three when the experimental temperature exceeded the boiling point of water. This increase is related to the expulsion of clay-bound water and subsequent availability of clay surfaces for methane adsorption.

Total gas in place (TGIP) is the sum of free and adsorbed gas volume estimates. The interaction and overlap of pore space between these two volume components are also important to consider. It is proposed to use a simplistic monolayer-based correction of volume of adsorbed gas from the free-gas volume based on a composite pore-size distribution from scanning electron microscopy (SEM) point-counting and nitrogen-adsorption data.

Pressurized sidewall-core samples were acquired at reservoir conditions to measure free- and adsorbed- gas volumes during controlled depressurization under laboratory conditions. This provided a baseline measurement for comparison with calculations from traditional measurements to understand which laboratory protocol and sample preparation technique provided the most robust results.

This study has elucidated methods to reduce the uncertainty in gas-in-place calculations and better understand resource distribution in dry-gas source rocks.

Operators face the continuing challenge to improve drilling efficiency for cost containment, especially in deepwater drilling environments where drilling costs are significantly higher. Innovative drilling technologies have been developed and implemented continuously to support the initiative. In many areas of the world, including the Gulf of Mexico (GOM), hydrocarbon reservoirs exist below thick non-porous and impermeable sequences of salt that are considered a perfect cap rock. However, salt poses varied levels of drilling challenges due to its unique mechanical properties.

At ambient conditions, the unconfined compressive strength (UCS) of salt varies between 3,000 to 5,000 psi; however, the strain at failure for salt can be an order of magnitude higher when compared to other rocks. Consequently, during drilling salt's viscoelastic behavior requires that its must be broken with an inter-crystalline or trans-crystalline grain boundary breakage. When compared to other rock types, the unique isotropic nature of salt results in a level of strain that is much higher for the given elastic moduli. This strain level makes salt failure mechanics different from other rock types that are prevalent in the GOM.

Hybrid bits combine roller-cone and polycrystalline diamond compact (PDC) cutting elements to perform a simultaneous on-bottom crushing / gouging and shearing action. Two divergent cutting mechanics pre-stresses the rock and apply high strain for deformation and displacement, resulting in highly efficient cutting mechanics. To meet the drilling objectives, different hybrid designs have been implemented to combine stability and aggressiveness for improved drilling efficiency. An operator, while drilling salt sections at record penetration rates, has successfully used this innovative process of rock failure utilizing the dual-cutting mechanics of hybrid bits. This has resulted in significant value additions for the operator.

This paper analyzes field-drilling data from successful GOM wells and attempts to correlate salt failure mechanics and provide insight into dual-cutting mechanics and its correlation with salt failure. The paper also reviews the drilling mechanics of hybrid bits in salt and highlights importance of dual-cutting mechanics for achieving higher penetration rates in salt through improved drilling efficiency.

Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by (Rivero, 2019). With the enhanced resolution of a reprocessed 3D seismic volume, more accurate seismic interpretation was completed to better delineate the internal and external structure of the reefs. The petrophysical analysis and core interpretation showed that the two reefs could be divided into two zones; the bottom zone has low porosity and the upper zone has high porosity that was targeted in previous well completion schemes. These zones were easily picked on well logs and when using Seismic Ant Tracking attributes, were accurately interpreted within the seismic volume. With the framework of the geomodel developed, rock type, porosity, permeability and water saturation were interpolated within the reservoir. Because natural fractures in these carbonate reservoirs are known to be an important part of fluid movement, it was important to characterize the discrete fracture network. In one well, a borehole image successfully quantified the properties of the natural fracture network. The observed fracture density (5 fractures/m) suggested discreate fracture zones throughout the well which was also confirmed with core fracture mapping. As part of the geomodel, a discrete fracture model (DFN) was generated; Seismic Ant Tracking was used to interpolate the fracture intensity within the reservoir. In these Devonian Pinnacle Reefs, and in other reservoirs, before investing in an EOR scheme, it is critical for the operator to understand the geologic structure and the petrophysical characteristics of the reservoir in as much detail as possible. This paper demonstrates how log and seismic data that is up to 40 years old can be converted to modern data types and be used to characterize a reservoir in a way not possible before.

The lacustrine delta sandbody deposited in the north of Albert Basin is unconsolidated due to the shallow burial depth, which leads to an ultra-high permeability (up to 20 D) with large variation and poor diagenesis. Log derived permeability differs greatly with DST results. Thus, permeability simulation is challenging in 3D geomodeling. A hierarchical geomodeling approach is presented to bridge the gap among the ultra-high permeability log, model and DST results. The ultimate permeability model successfully matched the logging data and DST results into the geological model.

Based on the study of sedimentary microfacies, the new method identifies different discrete rocktypes (DRT) according to the analyis of core, thin section and conventional and special core analysis (e.g., capillary pressure). In this procedure, pore throat radius, flow zone index (FZI) and other parameters are taken into account to identify the DRT. Then, hierarchical modeling approach is utilized in the geomodeling. Firstly, the sedimentary microfacies model is established within the stratigraphic framework. Secondly, the spatial distribution model of DRT is established under the control of sedimentary microfacies. Thirdly, the permeability distribution is simulated according to the different pore-permeability relation functions derived from each DRT. Finally, the permeability model is compared with the logging and testing results.

Winland equation was improved based on the capillary pressure (Pc) data of special core analysis. It is found that the highest correlation between pore throat radius and reservoir properties was reached when mercury injection was 35%. The corresponding formula of R35 is selected to calculate the radius of reservoir pore throat. Reservoirs are divided into four discrete rock types according to parameters such as pore throat radius and flow zone index. Each rock type has its respective lithology, thin section feature and pore-permeability relationship. The ultra-high permeability obtained by DST test reaches up to 20 D, which belongs to the first class (DRT1) quality reservoir. It is located in the center of the delta channel with high degree of sorting and roundness. DRT4 is mainly located in the bank of the channels. It has a much higher shale content and the permeability is generally less than 50 mD. Through three-dimensional geological model, sedimentary facies, rock types and pore-permeability model are coupled hierarchically. Different pore-permeability relationships are given to different DRTs. After reconstructing the permeability model, the simulation results are highly matched with the log and DST test results.

This hierarchical geomodeling approach can effectively solve the simulation problem in the ultra-high permeability reservoir. It realizes a quantitative characterization for the complex reservoir heterogeneity. The method presented can be applied to clastic reservoir. It also plays a significant positive role in carbonate reservoir characterization.

The unconventional reservoirs (UCRs) play a key role in global oil and gas supply. However, their reservoir characterization is difficult because of complex pore structure and low permeability-viscosity ratio. Usually, traditional techniques hardly can be used for determination of pore structure and estimation of reservoir properties. In this case, digital rock analysis (DRA) shows the potential for capturing detailed pore structure information and simulating rock properties, such as porosity, permeability, electrical properties and elastic properties. Recently, artificial intelligence (AI) techniques have presented an ever-increasing trend in a wide variety of research and commercial fields. Many AI applications can free man from the labor of complicated works in some way. Machine leaning (ML), which is a subdivision of AI, has attracted researchers' attention and been widely used in geoscience and reservoir characterization, such as feature extracting, rock type prediction and reservoir property estimation. The incorporation of AI and DRA is becoming an inevitable development trend for future reservoir study. In this paper, firstly, DRA workflow for reservoir characterization is introduced; secondly, the commonly used ML algorithms in DRA study is reviewed; finally, a case study of characterization of a tight carbonate reservoir with ML algorithm and DRA is presented. The analysis shows that ML can be applied in any part of DRA progress such as image segmentation, feature detection, rock image classification, numerical simulation and result analysis. Compared with traditional DRA algorithm, ML-based methods can reduce manual operation that has greatly impact on the results. The combination of ML and DRA provides a new insight in UCRs characterization and outlook the future opportunities of AI to solve the oilfield problems.

Production of oil from pilot shale wells has generally increased by implementing huff-and-puff (H&P) gas injection. The objective of this paper is using a new 3D, 3-Phase, physics-based, multiporosity model for matching and understanding primary oil production as well as recovery by H&P gas injection from a pilot well in the Eagle Ford shale.

History matching and performance forecast are carried out with a newly-developed fully-implicit 3D multi-phase modified black-oil finite difference numerical model, which uses a multiple porosity approach. "The model is capable of handling five storage mechanisms, including (1) organic porosity, (2) inorganic porosity, (3) natural fracture porosity, (4) adsorbed porosity, and (5) hydraulic fracture porosity" (Lopez Jimenez and Aguilera, 2019). Furthermore, the model has capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic walls, and stress-dependent properties of natural and hydraulic fractures.

These storage and fluid flow mechanisms, as well as the stress-dependency of hydraulic fractures, are widely recognized in the case of some shale petroleum reservoirs. Their inclusion in our simulation model permits evaluating the effect of these mechanisms during H&P gas injection. Results of the simulation, presented as cross-plots of production rates and cumulative production vs. time, indicate that oil recovery from shale petroleum reservoirs can be increased significantly by H&P gas injection. The possibility of desorption and gas diffusion is investigated.

The approach implemented in this H&P history match of an Eagle Ford pilot well should prove of value for simulating complex shale reservoirs.

Acquisition of fluid samples using wireline formation testers (WFTs) is an integral part of reservoir evaluation and fluid characterization. The increasing complexity of fluid sampling operations, especially in remote or offshore fields, requires a careful planning process involving systematic de-risking of the sampling objectives through quantitative evaluation of sampling hardware performance under uncertain downhole conditions and reservoir properties. During job execution, the cleanup of mud filtrate is monitored using downhole fluid analysis (DFA) sensor measurements. In addition to quantifying produced contamination and providing guidance for real-time decisions, these measurements hold valuable information about formation and fluid properties that can be extracted through advanced interpretation workflows.

In this paper, we demonstrate how a quantitative, model-based workflow was applied to both planning and interpretation for a series of sampling jobs in a remote and harsh environment. At its core, the workflow consists of high-resolution numerical flow models for the filtrate cleanup process that cover both conventional and focused sampling tools. To enable real-time, interactive, and probabilistic workflows, we use machine learning techniques to construct fast, high-fidelity proxy models, which, after thorough validation, replace numerical simulation in the workflow. Finally, the workflow employs methods for uncertainty quantification, global sensitivity analysis, and model inversion.

During the pre-job planning phase, the model-based workflow was used to select and mobilize the optimal sampling hardware, estimate sampling time uncertainty, and pinpoint the dominant sources of this uncertainty through global sensitivity analysis. After successful sample acquisition, the DFA measurements were reconciled with the cleanup model and the petrophysical evaluation to extract additional value from the measurements. Using measurements of water-cut and pressure, and conditioned to the petrophysical evaluation, the cleanup model was inverted for two-phase relative permeabilities. This recently developed methodology complements laboratory measurements of relative permeability on core samples.

Building on previous work in this area, this paper demonstrates the practical application of advanced planning and interpretation workflows for downhole fluid sampling. The methodology presented couples traditional, full-physics flow modeling with modern machine learning techniques to achieve highly agile workflows, enabling operators to more efficiently plan sampling jobs and extract value from the measurements.

Oil production from shale and tight formations will increase to more than 6 million barrels per day (b/d) in the coming decade, making up most of total U.S. oil production (> 50%). However, achieving an accurate formation evaluation of shale faces many complex challenges. One of the complexities is the accurate estimation of shale properties from well logs, which is initially designed for conventional reservoirs. When we use the well logs to obtain shale properties, they often cause some deviations. Therefore, in this work, we combine cores and well logs together to provide a more accurate guideline for estimation of total organic carbon, which is primarily of interest to petroleum geochemists and geologists.

Our work is based on Archie's equation. Resistivity log will lead to some incorrect results, such as total resistivity, when we follow the conventional interpretation procedure in well logs. Porosity is another complex parameter, which cannot be determined only by well log, i.e. density, NMR, and Neutron log. Therefore, the flowchart of TOC calculation includes five main parts: (I) the shale content calculation using Gamma log; (II) the determination of shale distributions using Density and Neutron logs and cross-plot; (III) the calculation of total resistivity at different distribution types; (IV) obtaining porosity using core analysis, NMR and density logs; and (V) the calculation of TOC from modified Archie's equation.

The results indicate that the shale content has a strong effect on estimation of water saturation and hydrocarbon saturation. Especially, the effect of shale content is exacerbated at a low water saturation. A more accurate flowchart for TOC calculation is established. Based on Archie's equation, we modify total resistivity and porosity by combining Gamma Log, Density Log, Neutron Log, NMR Log, and Cross-plot. An easier way to estimate porosity is provided. We combine the matrix density and kerogen density together and obtain them from core analysis. Poupon's et al. (1954) laminar model has some limitations when applying in shale reservoirs, especially at a low porosity.

Literature surveys show few studies on the flowchart of TOC calculation in shale reservoirs. This paper provides some insights into challenges of well logs, core analysis in shale reservoirs and a more accurate guideline of TOC calculation in shale reservoirs.