Acknowledgment

We would like to thank the Inter Rock board of directors for allowing to develop and to publish this workflow, making possible to add value to specific procedures to find the more efficient links between static and dynamic reservoir modeling. Additionally, we give thanks to the Petroleum Engineering Department of “Universidad de Oriente” (Puerto La Cruz-Venezuela), especially to Professors Roberto Salas, Oly Guerra, Mario Briones, Tania Gonzalez, Gisela Lopez, and Yuraima Parra, as well as UDO & IUPSM Professor Aquiles Torrealba, for the support, collaboration, affection, and trust they placed during the development of previous workflows, used as references for this integrated work.

Introduction

Rock Type approach, as well as storage and flow capacity determination, are “Key Factors” to assess the quality of reservoir rocks, which, among other things, is determined by the pore volume, the pore throat size distribution and the permeability. (Hartmann & Beaumont, 1999). Eventually, the process to determine reservoir producible fluids could take considerable time, since the expected results, both numerical and graphical, need certain knowledge, procedures, tools and/or software that hardly ever are available “all at once”, and when available, could be done separately at best, usually making this a non-so straight forward procedure to achieve a complete and satisfactory assessment. This suggests the need of an express workflow that performs a preliminary evaluation in a more expeditious manner, which also would streamline the process and minimizes uncertainty in the results. All this, in order to speed up turnaround time and expeditiously generating valid technical options to facilitate early wins in decision-making process. The mentioned workflow is based on the ranking and selection of prospective reservoirs in a given well, generating a prioritized list of flow units through an integrated and detailed petrophysical analysis to provide reliable results in a timely manner. For this, the input data consisted of Capillary Pressure, Relative Permeability and that previously generated by EPP and PetroFlow System applications. The main purposes for flow units ranking which, at the same time, are based on rock quality and producible fluid saturations, are optimizing the timing, reducing costs and minimizing error margin. In order to establish an ultimate Flowchart to eventually Implement the resulting workflow, it was required a full understanding of current process of fluid saturation evaluations in oil fields, the optimization of producible fluid saturation assessments through Fluid mobility analysis and a carefully documented logical sequence of applied physical and mathematical theories to obtain optimum results from selected input parameters.

Background

G.W. Gunter et al., SPE, Amoco EPTG in 1997, developed a work that consisted of early determina- tion of reservoir flow units using an integrated petrophysical method. This paper used case histories to introduce a graphical method for easily quantifying reservoir flow units based on geologic framework, petrophysical rock/pore types, storage capacity, Flow capacity and reservoir delivery speed. They concluded the earlier in the life of a reservoir this process is used, the greater the understanding of future reservoir performance. One of the most im- portant results in this process was the possibility to employ the least number of flow units and still keep honoring the character of the “foot by foot” data for simulation studies.

Álvarez, H., in 2003, developed a work that consisted of models for the interpretation of pressure tests as a tool for estimating the characteristic parameters of reservoir drainage areas. These models relate hydrocarbon production to downhole pressure changes, considering the geometric shape of the drainage area, formation characteristics, as well as boundary conditions.

Quaglia, A.; Panesso, R.; Su- bero, R.; Malave, L.; González, Z.; JOLEIP. 2004. This work con- sisted in the implementation of an integrated workflow through a portable system in order to sup- port the decision-making process based on preliminary petrophysical properties and subsequently on petrofacies determination which indicated Reservoir Quality, Hydrocarbon storage capacity & Flow capacity in order to estimate the amount of existing hydrocarbon and the percentage of most probable Fluids Flow. This system was developed using “The CASE methodology”, using Borland Delphi 7 as a coding and compilation tool for EPP App, with the InterBase 7 database manager. While MERISSE methodology was used in the development of the Petro Flow System; Visual Basic 6.0 software as a coding and compilation tool for PFS.[5]

Claib, A. and Presilla, R., SPE Student Chapter Seminar 2010. Amin Claib & Ricardo Presilla developed a software to determine and classify Flow Units base on Rock Quality and Producible Fluids Saturation in Hydrocarbon Reservoirs as part of internal resources for Inter-Rock Company. Automated science has gained adherents, and consequently, they have given the implanted systems reliability, therefore, their applicability in important areas and their influence on decision-making. Automated Support Systems combine sophisticated analytical models and data to support the different stages of the decision-making and evaluation processes.

Previous Workflows.

The Producible Hydrocarbon Saturation Workflow (S.H.P.) by its acronym in Spanish, has been developed following the Object-Oriented Methodology, based on previous Soft ware life cycles and a final Unified Flowchart Model, allowing the application of one integrated system with some ideas from the following previous stages:

1. Preliminary Petrophysical Evaluations. This first stage starts with preliminary petrophysical evaluations applying EPP_1.0. workflow (Figure 4), in order to determine “geological units” and their petrophysical properties: volume of clay, effective porosity, water saturation and permeability. It can be implemented “anywhere-anytime”. Preliminary QC Well log curves are processed using EPP_1.0 application immediately after a new well has been logged, although for the purposes of this phase, petrophysical properties can be also obtained from any commercially available software that meets the requirements. 

SFP Workflow was based on Well data provided by INTER-ROCK’s internal Rock Catalogue. Selected Wells for this study were assigned the names: “P01, P02….” and the subsequent studied flow units are represented with the letter “B” followed by a two- digit integer between 01 and 07, for example: “B01” and so on; the numbering depending on the number of flow units studied, having a maximum of 7 flow units for the selected wells, representing different Rock Types according to either quality index or pore throat radius. The required data comes basically from two sources: 1.- PetroFlow System 1.0, from which the following were used: depths, average formation water saturations and Rock Type classification (K/Phi or Winland R35), and 2.- Special Core Analysis: absolute and relative permeabilities and capillary pressures. Additionally, the studied reservoir is (at the time of the evaluation) in a state of sub-saturation, that is, whose pressure has not yet reached saturation pressure and there is no free gas. Key wells have been evaluated using the PetroFlow System 1.0 software, of which classification of flow units has been obtained according to the pore throat radius (Winland R35); considering that said, wells belong to the studied reservoir, would comply with the previous requirement, which would have drilled & cored different rock types (Megaporous, Macroporous, Mesoporous, Microporous, Nanoporous). For the purposes of developing the Workflow, the patterns of nanoporous rocks are also considered, however this work does not include this rock type for producible fluids saturation because they are not considered “Reservoir Rocks”, at least in conventional reservoirs as the ones studied here.

Methodology

Regarding the Data Review & Analysis: the data provided by PetroFlow System 1.0 outputs, have already been refined and validated as part of the previous workflow, where complete dB QC was performed on Cores, Logs and Production data; however, new lab data considered for Producible Fluids Workflow (SFP) required review and QC as well. It was mainly based on Routine Core Analysis (RCAL), Oil – Water relative permeability and Capillary Pressure curves on selected cores according to diverse Rock Types (Pore Geometry); in order to definitely select and classify the proper set of curves honoring the typical behavior for each rock quality along with the Production Data which consisted of mainly Initial Well Tests.

Relative Permeability Curves were selected at the beginning of this stage; there were twenty (20) diffe-rent core samples that fully complied with the selec ted parameters. The 20 samples belonged to different depths and rock qualities, honoring the five (5) rock types considered for this study. Specifically, Mega, Macro, Meso, Micro and Nanoporous rocks. To achieve the successful selection of the relative permeability curves, all the curves were grouped in order to compare their behaviors. Figure 8. 

Figure 9. Oil-Water relative permeability curves set from selected Core Samples & classified by rock type. (Laboratory Data). (Megaporous, macroporous, mesoporous and microporous). 

Then, it was possible to rank flow units by producible fluids proportion, where Relative Permeability, capillary pressure and fractional flow charts classified by rock type were used in the present work. A matrix was built to illustrate the rock quality variation of representative samples from conventional and special core analysis. The shown matrix in figure 12 has four lines and four columns and it’s possible to observe a single property variation along the lines and the different tests results representation along the columns. First Line shows porosity-permeability relationship variation, second line shows capillary pressure tests, third line shows relative permeability tests and fourth line the resulting calculation of Fractional Flow. Columns from left to right show tests variations for B6, B5, B3 & B2 samples respectively.

The saturation of producible fluids is obtained based on the physical principle that the sum of the volumes of fluids that occupy a given space is equal to unity or 100%; Therefore, when subtracting the sum of the irreducible water saturation and residual oil to unity or to 100%, a resultant percentage of fluid that, from a petrophysical point of view, would have no restriction from being produced, will be called “saturation of producible fluids” in the present work; that is, the percentage that can be extracted from the porous media using either current methods or future technology. Once producible fluids have been quantified, these are ranked considering the reservoir water saturation at the time of the study (referred to, in the present work, as “current water saturation”), provided by the output data of previous PetroFlow System 1.0 workflow, which uses current available Well Logs and Well production Tests. When comparing the percentage of irreducible water saturation with the percentage of “current water saturation”, movable water saturation is obtained by difference. For this model, oil-water relative permeabilities were used and compared at “current water saturation”. Once fractional flow of water was obtained, “movable” water and oil proportions are discretized in the percentage of fluids that are produced or will be produced when the test has been stabilized. This is achieved because the value obtained from the fractional flow of water represents the percentage of producible water that is able to flow through the pore space, from which an approximate percentage of oil in motion can be obtained by difference, having in mind that the sum of both percentages must be equal to 100% “producible fluids”. Subsequently, the flow units are ranked according to rock quality indices and saturation of producible fluids, considering the existing fluid saturations at the time of the study as well. The ranking process is performed by classifying flow units with the highest proportion of producible fluids in descending order. Figure 13. 

on pore throat sizes, but also be able to rank flow units according to their rock quality and the Oil-water Ratio of “producible fluids”. For this, it was necessary to have good quality well logs and both, conventional and special core analysis. Among the main advantages of the integration of these workflows are the optimization of the turnaround times of the processes and the efficient use of resources. It is important to highlight that this process has been supported by available laboratory data even though it could have been performed using analogs from inter-Rock’s catalog of rock sample analysis depending upon the characteristics of the reservoirs to be studied. In this case, it has been possible to hierarchize Flow Units not only by their rock quality, but also by the proportion of movable fluids, including Oil-water ratio of producible Fluids, resulting in an Integral workflow that we have called “Saturation of Producible Hydrocarbons” (S.H.P.), by its acronym in Spanish. Figure 15. 

It’s very important to characterize in detail the reservoir quality by formation. That’s why Capillary pressure is a very valuable test to count on because you would know the current water saturation at any stage of reservoir depletion, so you can determine original oil water contact and current oil water contact, or if it is thought that deterministic water saturation is right, then the hydrocarbon-water contact can be inferred.

Figure 16 shows the resulting Flow Unit Characterization spreadsheet based on numerical analysis according to producing hydrocarbon criteria. It is important to mention that, in a prediction exercise the hydrocarbon fractional flow for Mega & Macroporous Rocks tends to equal to about 17% when Sw = 50%.

Results were documented in a report and after that, Inter-Rock Technical Department was in charge of making the comparison with production tests data, obtaining quite acceptable results with very little differences considering the following aspects:

• The information used by the designed tool comes partially from laboratory analysis and al- though they optimally simulate the reservoir conditions, there might be often a margin of error, which undoubtedly affects any subsequent analysis.

• The aforementioned production tests offer volumes of fluids measured on the surface, which undoubtedly present differences with the volumes that are (at the same date) being contributed by the studied formation, this is due to the fact that in the journey inside the well (from the reservoir face to the surface) a series of phenomena are evidenced such as reservoir damage (variable degree), energy losses (pressure drops) and subsequent compositional variations and/or phase changes; which in one way or another interact in the fluid directly affecting the production measured at the surface; generating differences with the estimated flow capacity by the developed tool which would represent the flow at reservoir level.

• In some cases, two or more layers are producing “in comingle” so the percentages of fluid obtained at reservoir level are estimated through mathematical applications and well tests, which will hopefully con- tribute to reduce of the error margin.

• This work was done at matrix level, so if fractures and water coning are present, there will be uncer- tainty generating differences between the obtained results and the real values.

Likewise, it is important to emphasize that the obtained results are determined at a certain reservoir production stage (at the time of the study) so they vary over time, because fluids will be produced and relative permeabilities will change. The Certainty for the obtained Saturations of Producible Fluids by the designed tool was quite acceptable. Comparisons between theoretical Oil-water Ratio and real Well Tests were classified on a scale represented as follows:

a)   Good (between 75% and 90% of the Well Test Production report)

b)   Very good (greater than or equal to 90% of the Well Test Production report)

The comparison between calculated and Real Oil-Water Ratios from Well Tests were observed by Rock Type. Megaporous rocks: 87% (good). In this case the % of Water was higher than expected Macroporous Rocks: 94% (very good); Mesoporous rocks: 89% (good), in this case there was some uncertainty about reservoir pressure; Microporous Rocks: (no production reported). Based on the above, it is possible to affirm that the developed tool offers good information and reasonable certainty.

Another advantage of this workflow is that it could be applicable even without having special core analysis, such as capillary pressure and relative permeability, that is, using instead, analogues, either coming from any type of catalog or from similar reservoir projects, as long as it’d be possible to account for geological nature, porosity and permeability. This implies classifying the rock types and finding the right analog or the model to compare most accurately the flow units that represent better the reservoir, that is, the best correspondence of the rock-fluid relationship. Then you can find relationships between irreducible water saturation, pore throat size or simply by comparing rock quality. It may be also a good idea to subdivide the rock quality ranges, or simply classify them by flow capacities within the same reservoir rock.

Producible Hydrocarbon Saturation (SHP) has been designed to integrate the previous EPP (Pre- liminary Petrophysical Evaluation), PFS (PetroFlow System) and SFP (Producible Fluids Saturation) workflows as strategical optimization to support early Well testing and completion programs. As we all know, Completion and Production programs are not done until some type of numerical prediction of production test is performed, which might include nodal analysis. In any case, this would take time before an optimized completion scheme is available. It must be borne in mind that the best quality rocks, that is, those with the highest flow capacity (Kh), will be those that will produce their content more quickly, including the water influx while in transition zone or approaching its depletion stage, for which the necessary water management resources at the surface will have to be considered. The lower the quality of the reservoir rocks, the lower its production rates, but at the same time they will retard their water production in unwanted volumes due to their moderate to low flow capacity (Kh). Communication between flow Units is another important thing to consider, as well as the hydraulic behavior regarding fluid and pressure gradients. Anyhow, it will be an economic study that finally defines what should prevail, either good hydrocarbon rates with eventual high-water influx or low hydrocarbon rates with low or no water production.

The next example shows a “Fining Upward Sequence”, which gave rise to rock of various qualities. As shown in figure 18, five (5) categories of rock types from Nanoporous up to Megaporous are present. Coinciding, in this case, that the quality of the Rocks increases with depth. A good set of available data made possible to characterizing the flow units by integrating petrophysical properties as porosity, permeability, irreducible water saturation, and saturation of producible hydrocarbons at current water saturation. The analyzed wells were producing very little water since they were located up high in the hydrocarbon column, that is, close to irreduccible water saturation. It is expected that Reservoir rocks will increase the amount of movable water with depth, especially as the rock quality improves. Finally, this workflow will be very convenient in the application of petrophysical evaluations and flow units determination to study old and new wells where production optimization is required, either for well testing or well completion and re-completion programs. Figure 18. 

The same example, but this time showing flow units storage capacity is represented in Figure 19. As can be seen in each unit, it has been possible to represent the types of fluid and their conditions with respect to mobility, that is, non-movable and “producible” fluids representing 100% of the storage capacity. Each type of rock has a different fluid distribution that depends mainly on porosity, permeability and pore throat sizes. In each of the categories of rocks present, the different proportions of movable and non-movable fluids are shown, such as: irreducible water, residual oil and, by difference, the movable fluids (water + producible hydrocarbon) based on the petrophysical characterization which uses Well Log Analysis, routine and special core analyses. In this case, a graphical method derived and modified from the flow unit profile known as “Lorenz Plot” is used to identify flow units or “Petrofacies” from the studied geological sequence. This procedure focuses on a quantitative analysis that somehow facilitates the construction of a dynamic reservoir model, as well as the possibility of grouping flow units of similar quality while still honoring the geological features of the section under study. As mentioned before, if there were no core data to calibrate parameters, this model would work effectively using analog models. 

A flow unit should be understood as an interval with favorable petrophysical properties to “transmit” fluids being physically differentiable from other units based on their flow capacities. Flow units are stratigraphically mappable eventually having important variations in their lateral continuity. For this work, the flow units are considered indistinctly as “Petrofacies” or “Rock Types” which are defined as representative reservoir units with a different porosity-permeability relationship and a characteristic water saturation for a given height above the free water level. It is important to be aware about the geological context on which a study of this type is performed since the approach taken will depend on it. The geological context allows the flow units to be interpreted with in a model that considers variations in rock quality, sedimentary environments and, in a way, strati- graphic sequences rather than a simple well-to-well correlation. Additionally, this procedure allows us to identify potential sealing rocks, as well as zones of excellent flow capacity, which we have called “high speed zones”, which represent the best candidate units to optimize the reservoir production scheme, although on the other hand they can also generate early water breakthrough. This will depend on the hydrocarbon column height as well. This integrated method can be applied to any type of reservoir, whether consolidated or unconsolidated, siliciclastic or carbonaceous.

As explained above, this procedure attempts to provide options and more than that, tools to facilitate the numerical reservoirs simulation, as well as to support the decision-making process in both; preliminary petrophysical evaluations and early well completion programs.

In figure 20, it is shown how this procedure also works to determine Flow capacity of the previous identified units. Each unit not only show the magnitude of its flow capacity but also the proportion of fluid type that is able to move through each unit, that is, “producible” fluids representing 100% of the flow capacity. 

Conclusions

1.  A detailed analysis for the needs of the proposed system was performed, including documentation of a preliminary report of hierarchical flow units based on the saturation of producible fluids.

2.  The procedure used to classify flow units based on producible fluid saturations requires the intervention of multidisciplinary experts, as well as adequate and sufficient data.

3.  The results obtained in this work from both, rock samples and well logs have a high level of certainty mainly due to a rigorous organization and quality control process.

4.  It was possible to define an efficient mod- el for the system database that allowed to collect properly all the pertinent information for the required documentation of the results.

5.  The typical Swirr values found for the different rock types increased as a function of the de- crease in the pore throat sizes.

6.  The pore throat size directly affects the relative permeability of the fluids present in the porous media.

7.  The determination of fractional flow of water is a very helpful tool to estimate the percentage of water production at the studied formation.

8.  Viscosity and relative permeability of water and oil at a given reservoir water saturation has a direct impact on the fractional flow of water.

9.  High quality Megaporous and Macroporous rocks have higher productive capacity, so their percentages of producible fluids tend to change earlier than those of lower quality Mesoporous and Microporous rocks.

10. The certainty of the results depends directly on the conditions under which the studied well is producing. The modules that make up the system were efficiently developed, which allowed the Integrated Workflow to generated high certainty results in a timely manner.

11. The system was designed in a simple, friendly and consistent, which made easier the system-user interaction.

12. The application of this workflow saves time to users that need to make early decisions on preliminary petrophysical evaluations and well-completion programs.

13. This Workflow can also be performed using analogs from a “sufficient Quantity & Quality Catalog Data” as long as it’ll be possible to count on geological nature, fluid analysis, porosity, permeability, capillary pressure and relative permeability data.

14. Finally, it has been possible to hierarchize Flow Units not only by their rock quality, but also by the proportion of movable fluids, including water and producible hydrocarbons, resulting in an Integrated Workflow that has been called “Saturation of Producible Hydrocarbons” (S.H.P.), by its acronym in Spanish.

15. S.F.P workflow made possible to represent not only the fluid types but also their conditions with respect to mobility, that is, 100% of the storage capacity including non-movable and “producible” fluids, such as: irreducible water, residual oil and, by difference, the movable fluids (free water + producible hydrocarbon).

16. Each rock type has a different fluid distribution that depends mainly on porosity, permeability and pore throat sizes, based on the petrophysical characterization which uses Well Log Analysis, routine and special core analyses.

17. Comparisons between theoretical Oil-water Ratio from Fractional Flow estimations and real Well Tests, offered reason- able certainty since observed Well Tests by Rock Type allowed to document acceptable rate differences from the following results: Megaporous rocks: 87%, Macroporous Rocks: 94% and Mesoporous rocks: 89%.

18. Finally, S.H.P. integrated Workflow not only worked as to Flow capacity determination, but also to identify the proportion of fluid type that is able to move through each flow unit, that is, “free water” and “producible hydrocarbons” which represent 100% of the flow capacity.

Recommendations

1.   Consider the versatility and easy handling of the system for studies where capillary pressure and relative permeability data are available, guaranteeing maximum accuracy in the quantification of producible fluids depending on the rock type model.

2.   Apply Integrated approach in order to provide a greater degree of functionality, to search for certainty, efficiency and effectiveness of the results that are intended to obtain.

3.   It’ll be highly recommended to develop a further module that will allow, from this point; to determine surface production volumes; taking into consideration: Decline reservoir pressure, Possible flow restrictions, Eventual damage, Energy losses in the well and any other inherent factor that affects the movement of fluids from the reservoir to the surface, in addition to the respective economic evaluation.

4.   Even though this is an Inter-Rock’s “Internal Routine”, it is highly recommended that the User get in touch with Technology Department to go over the “user manual” before starting to apply the software.

5.   In order to save time & other resources it is recommendable to apply this workflow by teams in-charge of making early decisions on preliminary petrophysical evaluations and well-completion programs.

References

Álvarez G., Héctor E. “Identificación de Modelos de Interpretación de Pruebas de Presión Utilizando Redes Neuronales”. Trabajo de grado presentado en Maracaibo, estado Zulia. Universidad del Zulia. Facultad de Ingeniería. Escuela de Ingeniería de Petróleo. Departamento de Yacimientos. (2003).

Amott, E.: “Observation Relating to the Wettability of Porous Rock”, Trans. AIME (1959), Vol. 216, 156-

162.

Amyx B. and Bass Whiting. “Petroleum Reservoir Engineering”, McGraw-Hill Book Co., New York Toronto London (1960).

Arlow J., Neustad I. “UML 2 Programming”. First edition. Anaya Multimedia Editions. Madrid Spain. (2006).

Baker, O., and Swerdloff, W.: “Calculations of Surface Tension - 3: Calculations of Surface Tension Parachor Values.” Oil and Gas Journal, Dec. 5, 1955.

Barberi, E.: “El Pozo Ilustrado”. FONCIED, PD- VSA, Caracas (2001).

Bobek, J. E.: Mattax, C. C. and Denekas, M. D.: “Reservoir Rock Wettability - Its Significance and Evaluation.” Trans, Aime (1958), vol. 213, 155-

160.

Claib, A., and Presilla, R., “Software de apoyo para la determinación y jerarquización de unidades de flujo, en función de índices de calidad de roca y saturación de fluidos producibles en yacimientos hidrocarburíferos, en la empresa Inter-Rock. C. A.”. SPE Student Chapter Seminar 2010.

Craig Jr. F. F.: “The Reservoir Engineering Aspects of Waterflooding”, Henry L. Doherty Series. Mono- graph. Vol. 3 SPE, AIME (1970).

González, S., Zakyra, M. y Subero, A., Rafhaeld, J. “Sistema de Apoyo a la Toma de Decisiones en las Evaluaciones Petrofísica Preliminares con Determinación y Jerarquización de las unidades de Flujo para la Empresa “Rock Data Hunters, C. A.”. Trabajo de grado presentado en el I.U.P.S.M., Barcelona estado Anzoátegui (2003).

Gunter G. W. et al., Amoco EPTG. “Early determination of reservoir flow units using an integrated petrophysical method”. Annual Technical Conference and Exhibition held in San Antonio, TEXAS. SPE 38679. (1997)

Johansen, R. T. and Dunning, H. N.: “Relative Wetting Tendencies of Crude oil by the Capillarimetric Method”, Prod. Monthly (1959), 24 No. 9.

Johnson, E. F., Bossler, D. P. and Naumann, V. O.: “Calculation of Relative Permeability from Dis- placement Experiments,” Trans. AIME (1959). Vol. 1.

Malavé P., Lenis S. Agosto. “Sistema de Apoyo para la Toma de Decisiones en las Determinación de Petrofacies según la capacidad de Flujo y Alma- cenamiento de los Yacimientos para la empresa Rock Data Hunters, C.A.”. Trabajo de grado pre- sentado en el I.U.P.S.M., Barcelona estado Anzoátegui (2003).

Osoba, J. S., Richardson, J. G., Kerver, J. K., Hafford, J. A. and Blair, P. M.: “Laboratory Measurements of Relative Permeability.” Trans. AIME (1951).

Pirson, S. J.: “Oil Reservoir Engineering”, McGraw-Hill Book Co., Inc., New York City (1958).

Pittman, E. D. “ Porosity and Permeability Relationships to Various Parameters Derived from Mercury Injection - Capillary Pressure Curves for Sandstone”, The American Association of Petro- leum Geologists Bulletin, Tulsa, U.S.A., pp. 191- 198. (1992).

Quaglia, A.; Panesso, R.; Subero, R.; Malavé, L.; González, Z.; Sistema para la Toma de de- cisiones en Evaluaciones Petrofísicas y Jerarquización de Unidades de Flujo, en función de los Tipos de Roca. JOLEIP (2004).

Quaglia, A., Panesso, R., González, Z., Malavé, L., Subero, R. “Sistema modular de apoyo a la toma de decisiones en evaluaciones petrofísicas pre- liminares con determinación y jerarquización de unidades de flujo, en función de los índices de calidad de roca k/phi y r35”. Geominas, vol. 33, no. 37. Pp. 63-65. 2005.

Rivera, J..: “Prácticas de Ingeniería de Yacimientos Petrolíferos”. GEOPECA S.A. Puerto La Cruz, Venezuela (2004).