Palabras clave/Keywords/Palabras-chave:

Avaliações petrofísicas, capacidad de almacenamiento, capacidade de armazenamento, confining pressures, evaluaciones petrofísicas, petrophysical evaluations, petrophysical properties, presiones de confinamiento, pressões confinantes, propiedades petrofísicas, propriedades petrofísicas, storage capacity.

Overview

In the oil industry, it is essential to determine detailed petrophysical properties before characterizing reservoirs and finding out its economic potential, for which multidisciplinary teams and the use of new technologies are needed. Specific tools are utilized to allow predicting the quality and performance of reservoirs.

The core-to-log calibration is one of the primary practices that allows to improve the level of certainty of formations evaluation, since from the integration of the petrophysical properties measured in rock samples, either from conventional or special core analysis, it is possible to compare and furtherly calibrate with those obtained from well log analysis.

Commonly these core analyses are measured at 800 PSI confining pressure, which are considered Lab/standard conditions from where the petrophysical properties such as porosity and permeability are calibrated for reservoir modeling, ignoring the influence of overburden pressure.

That is why the purpose of this study is to develop a practical workflow for core-log calibration and a net confining pressure relationship that reduces the project timing without sacrificing the quality and certainty of the results, as well as demonstrating the effect of confining pressure on petrophysical properties and its incidence in the storage and flow capacity of the reservoirs.

This study is structured in two phases: First one is oriented to the calculation and calibration with laboratory data under both, standard and confinement conditions and a second one, related to the implication of those fore mentioned calculations and its effects in the characterization of the two studied reservoirs and their volumetric analysis.

The well log data was available in “.LAS” and “Excel” formats. The study wells had density, neutron and sonic logs, as well as resistivity, spontaneous potential, spectral gamma and caliper logs. Regarding the core data, there were conventional analyses, such as: core gamma, porosity, permeability, grain density, petrographic analyses and special analyses such as capillary pressures, that were necessary to perform the calibration of the petrophysical models. The available data for this study is subject to confidentiality policies, therefore, details of exact geographic location or real names of the wells will not be given.

The studied reservoirs are geographically located in South America and North America. A total of 4 wells were used, which are shown in table 1.

Background

Barbato, R., Panesso, R., Porras, J., & Quaglia, A. (2001). In his post “Is it possible to reach a detailed upscaling from a core-old logs match? The answer is yes, if an integrated approach is used. La Ceibita field, eastern Venezuela, case study” The objective of this study was to generate a detailed petrophysical model using a methodology based on the characterization of rock types, integrating cores, logs, production data, and geological and reservoir information, in order to optimize exploitation plans and validate reserves. The petrophysical characterization led to the definition of rock types, which were classified based on the pore throat radius estimated from mercury injection capillary pressure data.

Panesso, R., Quaglia, A. (2002). In their work, “Reserves re-estimation using Scal to validate SW model from neural net processed old logs. La Ceibita field, eastern Venezuela, case study”. The petrophysical characterization had the objective of the definition of rock types, which were classified according to the estimated pore throat radius from mercury injection capillary pressure data. In this paper, reserves volumes were calculated and successfully updated and increased after redefining the contact location.

Contreras, E. Garcia, P. (2007). In their publication “Importance of planning, cutting, handling and analysis activities of oil well drilling core” they address the relevance of cutting, handling, and laboratory analysis activities of oil well drilling cores for the evaluation of geological formations, in relation to the characterization, evaluation, and use of oil deposits. In addition, the important criteria are described when taking representative samples of formations and their correct handling, preservation and their possible implications in the determination of petrophysical properties and their relationship when establishing core-profile calibrations.

Castillo, A. Ríos, J. (2008). In their degree work “Petrophysical characterization from drilling core and well loglogs for the sands of the Mugrosa formation of the Campo Colorado, blocks I and II” said petrophysical characterization of the aforementioned formation, based on data obtained from logs, integrated with information obtained from drilling cores and determine the basic petrophysical properties such as porosity and permeability. They also cover the corrections of these properties by confining pressure to generate the petrophysical model, defining the hydraulic units and flow zone indicators.

Inter-Rock (2009) In their report “Integrated petrophysical model based on rock type” covers the methodology and procedures used that allowed generating said petrophysical model to improve knowledge of the reservoir using the integration of information from conventional, geological and special analyzes of cores, complemented with the interpretation of well profiles, geological environment and production data.

Andersen, M., Duncan, B., & Mclin R. (2013). “Cores in formation evaluation” In their publication, the authors explain the essential role played by drilling cores, through the analysis of rock samples obtained at the bottom of the well, and the determination of petrophysical properties such as, lithology, porosity, permeability, fluid saturation in core laboratories and the procedures involved, to help operators more specifically characterize the complex nature that a reservoir can present.

Quaglia, A., Montilva, A., Porras, J., Panesso, R. (2020). In their work “Comparative analysis for resulting petrophysical property ranges using different mercury injection capillary pressure methods”, they argue that a specific workflow must be followed for the samples selection. The procedures used in core analysis laboratories to calculate properties such as porosity, permeability can be estimated at standard laboratory pressure conditions but also perform corrections for net overburden confining pressure (NOBP) due to the fact that the results of these properties vary depending on the mechanical condition of each sample, consequently, when the overburden pressure increases, the pore space is compressed, the pore throats are reduced as well as the permeability. The magnitude of variation in these properties is largely determined by pore compressibility. This correction can be applied by establishing a relationship between the properties at reservoir conditions (NOBP) against standard laboratory conditions using a scatter plot.

Methodology

This study is defined as descriptive research, since, based on information from well logs, and laboratory tests of existing drilling cores, the petrophysical properties of a reservoir are described, allowing the core-profile calibration to be carried out. The results of such calibration allow “to have an immediate application in the solution of practical problems”, such as the determination of the implications of these properties, permeability and porosity, in the characterization of deposits. It is also considered as applied research.

According to most Authors, documentary research is a process based on the search, recovery, analysis, criticism, and interpretation of secondary data, that is, those obtained and recorded by other researchers in documentary sources: printed, audiovisual or electronic, as in all research, the purpose of this design is the contribution of new knowledge.

The population or universe to be studied is conceived as the space from which the sample to be used in the investigation will be extracted. For the purposes of this investigation, there are 72 wells located in South America, and 200 wells in North America, which serve as the object of study. It is important to note that, due to confidentiality issues, Inter-Rock reserves the right to publish the names of the companies, fields, and wells involved in the study. In order to achieve the expected objectives, it was necessary to establish a workflow shown in figure 1.

Balestrini (2006) stated that: The sample is a “representative subset of a universe or population”. In this sense, the sample of this project is made up of 4 wells in total, 2 wells located in South America and 2 wells located in North America. These selected wells have information from routine and special core analysis and well logs (GR, SP, Density, Neutron, Sonic, etc.).

Following the methodology workflow (Figure 1), a series of steps are briefly described:

1.- Literature review and selection of best reservoir nature driven equations to determine petrophysical properties: This activity included the bibliographic compilation and consultation of available references regarding the study wells. It is important to mention that this research was supported by the work carried out by Inter-Rock, C. A (Inter-Rock, 2009 and 2011). Regarding the selection of equations, it was necessary to organize and classify them in order to evaluate their applicability, because the available data limit the application of certain equations at the time of the analysis to determine petrophysical properties and parameters.

2.- Available Core & Log Data collection: The set of data collected is composed of well logs (acoustic, electrical, nuclear, lithological), and core data, (Routine and special analyses (porosity, permeability, grain density, fluid saturation, capillary pressure, among others). These data were received in “. LAS” and “Excel”, which were subsequently uploaded to the Interactive Petrophysics (IP) software.

3.- Identification and classification of data: This process basically consisted of the organization and categorization of all the existing data sets, for all the formations and each one case studies, including all the studied wells and its detailed core & log inventory.

4.- Data Quality Control: Quality control is determinative when it comes to the adequate certainty needed in petrophysical evaluations. This process encompasses different activities as stated by Inter-Rock, C.A (Figure 2). The data used for this project was previously subjected to quality control process, therefore, it was only necessary to perform depth adjustments and curve editing.

5.- Determination of petrophysical parameters and properties: to determine the petrophysical parameters, the measurements resulting from the electrical properties of cores in the “A1” and “G1” formations were used. (Inter-Rock, 2009 and 2011)

5.1.- Cementation exponent (m), saturation exponent (n), tortuosity factor (a): based on the study carried out by Inter-Rock, 2009 and 2011, the cementation exponent “m” was estimated by plotting the formation factor vs. Porosity, resulting in trends that honored the set of samples for the studied wells ALFA-1 and ALFA-2 as well as GAMMA-1 and GAMMA-2. From the intersection of the formation factor (Y axis), of the previously mentioned graph, for a porosity value equal to 1, the tortuosity factor was obtained, a=1 for all the wells under study. In this same way, regarding the saturation exponent “n”, it was calculated by plotting the resistivity index vs. Water saturation, thus obtaining the slope “n”. (Inter-Rock, 2009 and 2011)

5.2.- Geothermal Gradient: According to Inter-Rock, 2009, to determine the geothermal gradient of the study areas, data from Well log headers as bottom hole temperature and depth were used to calculate the geothermal gradient of the study areas from the following equation:

GG= (T_2-T_1)/(P_2-P_1)

Where:

GG = Geothermal gradient

T_2 = Bottom hole temperature

T_1 = Surface Temperature

P_2 = Bottom hole depth

P_1 = Surface depth

5.3.- Formation water resistivity “Rw”: the calculation of “Rw” is fundamental to estimate the water saturation “Sw” and this in turn allows obtaining the oil saturation. There are several methods from which this parameter is determined, such as Pickett plot, Through SP curve analysis, physical-chemical analysis of the formation water, this method being the most decisive as long as the samples are representative of the formation water. For this investigation, “Rw” values were taken from Inter-Rock’s 2009 and 2011 previous studies.

5.4.- Vclay Model: to determine Vclay model, it was necessary to estimate the gamma ray index (IGR) from the gamma ray log applying the following equation:

Where:

IGR=Gamma ray index.

GRlog=Gamma ray reading in the interval of interest.

GRmin=Gamma ray minimum ray reading.

GRmax=Maximum gamma ray reading.

Since the result of this IGR linear relationship was pessimistic, the clay volume was calculated using the Larionov Young Rocks equations for the ALFA-1 and ALFA-2 wells (Figure 3).

Where:

VclGR = Volume of clay from the Larionov Older Rocks equation

IGR = Gamma ray index

Clay Model is then calibrated based on the comparison of well log derived petrophysical properties against conventional and special core analyses. In this case the calibration of the clay model was performed based with available X-ray diffraction (XRD) data as a reference in areas where there was necessary to adjust the values of Corrected GRsℎ and Corrected GRclean, as well as other radioactive element curves as potassium, thorium, and uranium.

5.5.- Porosity model: to determine the porosity model in the ALFA-1 and ALFA-2 wells, the combination of Density-Neutron logs was used to determine porosity, as well as core analysis (grain density, XRD, Porosity). On the other hand, for the GAMMA-1 and GAMMA-2 wells porosity was determined by using density logs and core analysis (grain density, Porosity). Matrix density values obtained from core analysis (GD) were entered as averages for the A1 and G1 formations. Since the ALFA-1 and ALFA-2 wells had lithological description from mudlogging cuttings, it was possible to calculate the matrix density ρ_ma from the following equation (Inter-Rock, 2009):

ρ_ma=∑ρ_i*V_i

Where:

ρ_ma = Matrix Density

ρ_i = Density of the different minerals present in the formation

V_i = Volume (normalized to 100%) of the different minerals present in the formation, obtained by mudlogging cuttings description.

In the case of the wells that had X-ray diffraction (XRD) analysis, the density of the dry clay was determined by applying the following equation:

RHOBdryclay=ΣRHOclay*Vclay

Where:

RHOBdryclay=Density of dry clay, g/cm3.

RHOclayi=Density of each type of clay present in the samples, g/cm3.

Vclayi=Volume (normalized@100% from XRD), of different types of clay present in the sample (Table 2).

ρWetcl=Density of wet clay (g/cm3).

ρfl=Density of the fluid (mud filtrate) (g/cm3).

ρHyAp=Apparent hydrocarbon density (g/cm3).

Vcl=Volume of wet clay (dimensionless).

Sxo=Water saturation in the invaded/washed zone (%).

Apparent hydrocarbon density (g/cm3):

ρHyAp=2*ρhden((10-2.5*ρhden)/(16-2.5*ρhden))

Where:

ρHyAp=Apparent hydrocarbon density (g/cm3).

ρhden=Known hydrocarbon density (g/cm3).

Neutron Porosity: The equations used to calculate porosity from the Neutron log are shown below:

ΦN=(neu-Vcl*NeuMatrix-Exfac+NeuSal)/(Sxo+(1-Sxo)*NeyHyHI)

Where:

Exfact=(ρma/2.65)2*(2*Swx*(Φx)**2+0.04*Φx)*(1-Swx)

Φx=Φ+Vc*NeuCl

Swx=(Φ*(Sxo+(1-Sxo)*NeuHyHI)+Vcl*NeuCl)/Φx

NeuHyHI=9*ρhden*((4−2.5*ρhden)/(16−2.5*ρhden))

ΦN=Neutron porosity (%).

Φneu=Neutron value read on log (%).

Vcl=Volume of wet clay (VWCL).

NeuCl=Neutron value of wet clay (%).

NeuMatrix= Matrix in which the Neutron was recorded.

Exfact=Neutron excavation factor.

NeuSal=Neutron correction for formation salinity.

Sxo=Water saturation in the invaded/washed zone.

ρhden= Known hydrocarbon density.

NeuHyHI=Apparent hydrocarbon hydrogen index.

Density-Neutron Porosity: the equations used to calculate porosity from the combination of Density-Neutron logs are shown below:

ΦDN=ΦD1+((ΦN1−ΦD1)/(1−(ΦN1−ΦN2)/(ΦD1−ΦD2)))

Where:

ΦDN=Density-Neutron Porosity.

ΦN1=Neutron porosity corrected for matrix 1.

ΦN2=Neutron porosity corrected for matrix 2.

ΦD1=Porosity of the Density corrected for matrix 1.

ΦD2=Porosity of the Density corrected for matrix 2.

5.6.- Water saturation (Sw): for the determination of the water saturation model (Sw), this was calculated from the Archie equation for all studied wells.

Where:

Sw = Water saturation (%).

Rt = True resistivity of the formation (Ohm-m).

Φ = Porosity (%).

m = Cementation Exponent (dimensionless).

n = Saturation Exponent (dimensionless).

a = Tortuosity Factor (dimensionless).

Rw = Formation water resistivity (Ohm-m).

Water saturation (Sw) curves were calculated directly from logs and compared with Laboratory saturation analysis results. The described procedure was applied only to the ALFA-1 and GAMMA-2 wells because the other wells did not have core saturation analysis.

5.7.- Permeability: The permeability for the ALFA-1 and ALFA-2 wells was determined from the k/phi ratio obtained from core analyses. A core permeability (NOBP) vs. core porosity (NOBP) chart was made from which equation the below equation was obtained, which allows calculating permeability from porosity.

Where:

KCORE=Core Permeability (mD).

ΦCORE=Core porosity (%).

In the case of the GAMMA-1 GAMMA-2 wells, the permeability was calculated using the Schlumberger Chart K3 equation shown below:

K=10,000*(Phi4.5/Sw2)

Where:

K=Permeability (mD).

Phi=Porosity (%).

Sw=Water saturation (%).

6.- Determination of Petrophysical properties at Overburden Conditions (NOBP): Porosity is one of the petrophysical properties that is affected by confining pressures. The porosity results obtained from cores in the A1 formation were calculated for both cases (laboratory/standard conditions and overburden conditions), from which scatter plots were made (Figure 4) to establish the correlation between these conditions.

Where:

KNOBP=Permeability under overburden conditions (NOBP)

KSTD=Permeability at standard conditions (STD)

In the case of the G1 formation, an already established equation from a previous project carried out by Inter-Rock, C.A (2009) was used and applied to calculate permeability at overburden conditions (NOBP) from available permeability data at standard conditions (STD):

Where:

KNOBP=Permeability to overload conditions

KSTD=Permeability at laboratory conditions

The calibration of the permeability model was performed in the same way as the previous porosity model. A template was generated in which the permeability curves obtained from established equations with the help of core permeability from laboratory analysis at overburden conditions (NOBP).

Once porosity and permeability were calibrated at overburden conditions (NOBP) and based on previous studies carried out by Inter-rock, C. A, in 2009 and 2011, Pore throat radius relationships were determined for further classification of Rock Types. Pittman R40 equation resulted to be the best equation for the A1 formation, while Winland R35 was determined as the best equation to define the quality of the G1 reservoir:

Where:

R40=Pore opening radius (microns) corresponding to a mercury saturation of 40%. (Pittman)

R35=Pore opening radius (microns) corresponding to a mercury saturation of 35%. (Winland)

Kair=Air permeability (mD)

Φ=Porosity (%)

Rock Types definition: after having calculated the pore throat radii, the rock types were determined according to the classification shown in table 3 for the A1 and G1 formations:

Where:

h=Thickness (ft).

Φ=Porosity (fraction).

Sw=Water saturation (fraction).

Boi=Volumetric Factor Bn

The volumetric reserve estimate (OOIP) was calculated with both, the petrophysical properties at standard pressure (STD) and the one at overburden (NOBP) conditions.

8.- Implications of the core-log (standard vs overburden conditions) calibration on petrophysical evaluation and volumetric calculations: As previously mentioned, the study of the effects of confining pressures on petrophysical properties such as porosity and permeability is of a great importance in petrophysical evaluations and subsequently in reserves estimations. To determine the implications of the proposed workflow, two petrophysical models were generated, one of them using the data at laboratory conditions (STD) and the other one using the data at net overburden conditions (NOBP). Later on, results were compared through statistical charts that allowed observing the variations of petrophysical properties and its implications on the reservoir storage and flow capacity as well as the volumetric estimation of reserves.

As a result of the application of cutoffs parameters (volume of clay, porosity, and water saturation) shown in table 4, the petrophysical summaries were generated, for both reservoirs (A1 & G1) @ standard (STD) and overburden (NOBP) conditions. Tables 5, 6, 7 and 8, show average petrophysical properties (volume of clay, effective porosity, water saturation) either at standard (STD) and net overburden pressure (NOBP) conditions in the interval of interest.

Generally, the main implications after applying Net Overburden Pressure, to determining the petrophysical properties, are related to de “adjustment” or, in many cases, the decreasing effect on properties values. At the end of the day, the variation in the magnitude of the aforementioned properties would depend upon the mechanical properties of the rocks and fluids. This is performed to count on more realistic petrophysical properties at reservoir conditions and subsequently over hydrocarbon reserves estimation. The variations in these properties are mentioned below, allowing the model to be adjusted to reservoir conditions for each of the wells studied.

8.1.- Porosity variations: as a result of the porosity model calculated either at laboratory conditions (STD) and overburden conditions (NOBP), histograms of the effective porosity (PHIE) calculated at standard pressure conditions (red curve) and the effective porosity (PHIE) calculated at overburden conditions (black curve), are shown. It was possible to observe the variation of the porosity as a consequence of the application of net overburden pressure conditions. The average variation of the porosity was determined to be approximately 3 porosity units (P.U.) for wells ALFA-1 (right side histogram) and ALFA-2 (left side histogram), and 1 porosity unit (P.U.) for wells GAMMA-1 and GAMMA-2. (Figures 6 and 7, respectively).

8.2.- Permeability variations: As a result of the permeability model evaluated at standard conditions (STD), where frequency histograms are shown, the permeability curve at standard conditions (red curve) and the permeability at overload conditions (NOBP) for the ALFA-1 and ALFA-2 wells, respectively. From which it was determined that the average variation between the permeability obtained at standard conditions and the permeability at overburden conditions was 745 and 147 millidarcies, that is, a decrease in 42% for ALFA-1 and 18% for ALPHA-2. Figure 8.

Regarding the wells GAMMA-1 and GAMMA-2, permeability variations are moderately different than previous ALFA-1 and ALFA-2 wells. Histograms in figure 9 show permeability variations in Formation G1, as a consequence of the reduction of porosity by overburden/confining pressure. The red curves correspond to the frequencies of the permeability at standard conditions (STD) while the black curves correspond to the permeability at overload conditions (NOBP). Variation of permeability was 2.45 and 2.61 millidarcies, which represents 31% and 43% for the GAMMA-1 and GAMMA-2 wells respectively.

8.3 Pore throat radius variations: histograms are shown in figure 10 regarding the pore throat radius variations obtained from standard (STD – red curves) to overburden conditions (NOBP – black curves) for each of the A1 Formation wells. Pore throat Variations resulted in 4.57 and 2.87 microns, which represent a decrease of 29% for the ALFA-1 well and 23% for ALFA-2 well at overburden conditions (NOBP).

Histograms on figure 11 show the pore throat radius variations from standard conditions (STD – red curves) to overburden conditions (NOBP – black curves). These variations resulted in 0.35 and 0.38 microns, which represent a decrease of 20% and 25% regarding the pore throat radius curve calculated at overburden conditions (NOBP), for the GAMMA-1 and GAMMA-2 wells respectively.

8.4 Volumetric calculations of reserves: or also known as Original hydrocarbon in place for this work (OHIP) was obtained from the hydrocarbon reservoir thickness and the petrophysical properties average such as porosity and saturation, assuming an area equal to 1 acre (A= 1 acre) and volume factor equal to 1 (Boi=1 Bbl/STB), where 1 bbl (reservoir barrel) = 1 STB (Stock tank Barrel). As a result of applying the Cut-Offs parameters @ STD & NOBP conditions, it is observed that for A1 formation the (OHIP) at NOBP conditions decreased approximately 22% with respect to the (OHIP) at STD conditions, while for the G1 formation the decrease was approximately 14% (Table 9).

It is also important to highlight that this workflow becomes extremely important when it comes the time to get a realistic volume of reserves number in order to low the uncertainty in the determination of the original hydrocarbon in place (OHIP) which represent the most important factor in an asset evaluation. It will support a better budget plan and will aim for a more successful reservoir management. Among other things, there is the determination of the reservoir quality, which is evaluated through a rock type model and is determined from the pore throat radii that also vary depending on the conditions under which they are calculated; either at standard conditions (STD) or at net confinement conditions (NOBP), where it can be observed that with the reduction of the pore throat radius when applying the net effective stress, this directly affects the rock type classification resulting to be of almost always lower rock quality. In the case of the A1 formation, in both wells, it is observed that part of the Megaporous rocks became Macroporous due to the effect of the overburden pressure. In the same way, G1 formation decreases in rock quality with respect to the standard conditions due to partial reduction in pore throat radius due to the effect of overburden pressure. Figures 12, 13, 14 and 15 show pie charts with Rock Types variations from standard conditions (STD) to overburden conditions (NOBP).

It is important to mention that the best obtained fit was given when working under overburden conditions (NOBP). It was observed on statistical charts how petrophysical properties such as porosity and permeability, directly related to the flow and storage capacity of the reservoir, tend to decrease as a function of confining pressure, also associated with lithology, compaction, and stress regime.

Cutoffs parameters were those defined by Inter-Rock from previous projects, where the key wells for this investigation were included. Based on that, the net thicknesses and petrophysical summaries were determined either for standard conditions (STD) and net overburden conditions (NOBP) which resulted to be more realistic.

Most of the petrophysical properties were diminished by adjusting the calculations at NOBP. Permeability resulted the most sensitive property due to an important reduction of pore throat size in different rock types. On the contrary, Sw increased its value in all reservoirs after optimizing the net hydrocarbon thickness. It is important to highlight that A1 reservoir kept being the best quality reservoir after applying the workflow.

Regarding the volumetric calculation of reserves (OHIP) under overburden conditions, this decreased approximately 22% for A1 Reservoir, while in reservoir G1 the volume of reserves experimented a reduction of approximately 14%. Table 10 show variations in (%) of the petrophysical properties and the Hydrocarbon in Place.

For the great majority of the formation analysts, this workflow may appear a pretty straight forward procedure and that could be true; nevertheless, the main objective of this matter is related to its importance in reservoir characterization and volumetric estimations since most of the studies worldwide omit this issue, with the further consequences either in over estimating reserves or mislead economic feasibility studies.

Documentation of the workflow through a brief detailed description based on the necessary data for implementation was made as shown in figure 16. It consisted of 5 phases, which are described below:

Database: this phase of the procedure includes a detailed review of previous studies, related activities, data organization, identification of key wells and classification of the available information for the project database construction.

Log and Core Quality control: data quality control encompasses the methodology designed by Inter-Rock, including available Log Certification and Editing to ensure an adequate level of certainty.

Petrophysics calculations: this phase of the workflow determines the necessary petrophysical parameters and properties in order to obtain Vclay, porosity, water saturation permeability and Rock type models.

Core-to-Log Calibration: consists of performing the calibrations between properties and petrophysical parameters determined by well logs with respect to the properties and parameters measured in rock samples either at standard and/or confining conditions, and as necessary, making the pertinent adjustments. (Figures 17,18,19 and 20).