Category Archives: Knowledge Management

Understanding the High Value of Ontologies for Natural Resource Exploration

Companies recognize the value of the data captured through their history.  However, data is being collected in the last decades and organized under several different models conceived to attend tasks and applications. Besides structured data, documents, spreadsheets and images complement the inventory of strategic corporate information.

The situation of most large petroleum companies today is hundreds, maybe thousands of database tables supporting hundreds of applications and more than thousands of documents spread in virtual drawers. A set of tables reflects the process of well drilling, while another set describes reservoir characterization. More tables organize the information about reservoir quality, which is added to several proprietary files of reservoir modeling and analysis. Technical reports summarize the stratigraphic interpretation of the sedimentary basin and production reports register the amount of oil produced along with the expenses in facilities and maintenance.

Then a manager wishes to answer common questions like “Why the well 1 is producing less than the well 2?”, “Why the production decrease in well 3?”, “What are the factors that made the play X demands more maintenance than usual?”. The answers are there, into the data, submerged and hidden under models and applications that reflect different views over the information.

Then comes the big question: How can I extract some useful information from this data swamp?

Ontology is our response. Ontology is a theory based on Philosophy that helps in making explicit the intended meaning of a vocabulary according to a conceptualization of a community of stakeholders. When applied to build models that express this vocabulary, an ontology builds an artifact (an RDF or OWL file, or a structured document) that specifies formally the set of shared concepts used in the community. Ontology artifacts are machine-readable, which means that the explicit represented semantic can be used by computers to solve problems with no human intervention.

Ontologies are useful in at least three ways: 

  1. Standardize the way in which data is collecting, avoiding free reports that are hardly processed by computers;
  2. Brings out the semantics of stored legacy documents, allowing them to be integrated to support data analytics and insight engines.
  3. Indexing visual content of images, photographs and figures in old pdf data.

A good example of the situation (1) can be seen when comparing Figures 2 and 3. Figure 2 shows a description of a columnar section of Aruma Formation of Saudi Arabia [Al-Kahtany, El-Sorogy et al. 2016] and Figure 3 the description of a well in Campus Basin, Brazil. Both sections represent, in a pictorial way, the lithology, textures, sedimentary structures, fossil content and contacts of formations. However, the description in Figure 3 was produced with an ontology-based software, the Strataledge, which controls the sedimentary characterization in the description using a formal vocabulary.  Figure 3 itself was not drawn, but consist of one of the exportation formats that integrate the columnar information along with petrographic data of the rock. Contrasting with the human-made interpretation shown in the last column of Figure 2, the ontology allows that the depositional environment being automatic interpreted by the ontology-based system [Carbonera, Abel et al. 2015].

Figure 2 – Lithostratigraphic section of the Aruma Formation, Khashm Bowaibiyat area, Northeast Riyadh [Al-Kahtany, El-Sorogy et al. 2016].

Figure 3 – Stratigraphic and petrographic integrated study in Campos Basin. 

Although domain ontology plays an important role in standardizing and structuring corporate information, its value is strongly recognized when using to extract correlations of the stored information.  That was the case of applying data mining for the extraction of reservoir petrofacies from petrographic data, captured using Petroledge ontology. The tables exported by the system share the same vocabulary and format for the names of minerals and pores, textural aspects, and all the transformations suffered by the rock through diagenesis. The quantification of all the aspects of the rocks allows applying clustering techniques and feature selection to identify the petrofacies that are related to the level of porosity of the petroleum reservoir rock. The ontology and clustering techniques applied in the ongoing work of [Fernandes 2019] has achieved results close to those of experts doing the same task: 85% of similarity in the separated sets of machine and human expert.

However, the reality on the corporate environment is that the stored data does not share a consensual model based on ontologies and it still needs to be consulted for decision support.  In that case, ontologies can be used to build a semantic layer that represents the meaning of the main concepts of the organization. The role of this layer is to connect the human needs with metadata of structured data and to the content of documents.  Domain ontologies are associated with techniques of knowledge graphs, information retrieval and insight engines to provide a uniform way of automatically indexing legacy data. In the work of [Lopes, Alvarenga et al. 2019 ], the ontologies of Strataledge and Petroledge were applied to index pdf and doc documents. The documents were scientific journals on sedimentology, stratigraphy and technical reports of integrated projects on Sedimentology, Petrology, Seismic Stratigraphy and Biostratigraphy of Campos Basin in Brazil.  The ontology supported the word sense disambiguation for automatic indexing of the terminology of the document for information retrieval and data analytics.

Domain ontology provides very useful support for labeling and indexing visual content. Petroledge ontology supports the Rock Viewer application that captures and indexes photomicrographs of sedimentary rocks, based on detailed petrographic aspects of the rock: mineralogy, texture, crystal habits, contacts, fabric, diagenetic paragenesis and so on.  The software produces an album of commented pictures that can be retrieved and cross-correlated regarding any feature of the rocks that can be associated with reservoir quality.

Regardless of the effort in developing heavy well-founded ontologies, their use has proven to be valued to approximate the intention of the consultation in the mind of users with the meaning of vocabulary dispersed data lakes in corporate environments.   Strataledge ontology is built over 700 structured terms that cover all terminology on facies description for magmatic, metamorphic, chemical, clastic, and metasomatic types of rocks. A complete terminology of lithology, textures, fabric, and structure are specialized for each type of rock class and associated individually with a visual icon that represents each particular aspect.

Petroledge ontology includes more than structured 800 terms for all mineralogical classes, fabric, textures, diagenetic transformation, and structures found in the composition of magmatic, metamorphic, chemical, clastic, and metasomatic types of rocks, including Brazilian pre-salt lithological types.

Strataledge and Petroledge ontologies licenses are available to develop applications and databases.

Visit for more information about Endeeper ontologies.


Abel, M., E. S. L. Gastal, et al. (2019). “A knowledge organization system for image classification and retrieval in petroleum exploration domain.”

Al-Kahtany, K. M., A. S. El-Sorogy, et al. (2016). “Stratigraphy and depositional environments of the Upper Cretaceous Aruma Formation, Central Saudi Arabia.” Arabian Journal of Geosciences 9(5): 330.

Carbonera, J. L., M. Abel, et al. (2015). “Visual interpretation of events in petroleum exploration: an approach supported by well-founded ontologies.” Expert Systems With Applications 42(5): 2749-2763.

Fernandes, L. P. (2019). A clustering-based approach to identify reservoir petrofacies from ontology controlled petrographic data (provisory title) Master, UFRGS.

Lopes, A., R. Alvarenga, et al. (2019 ). Improving the Semantic Relatedness Evaluation through the Pre-Calculated Semantic Neighbors. International Conference on Tools with Artificial Intelligence – ICTAI 2019. R. Keefer. Portland, IEEE.

Smith, B. and C. Werner (2015). Aboutness: Towards Foundations for the Information Artifact Ontology. Sixth International Conference on Biomedical Ontology (ICBO), CEUR-WS. 1515: 1-5.

Magmatism in Sedimentary Basin – Application in Oil Exploration

The presence of igneous rocks within sedimentary basin has already been seen only as a hindrance to the occurrence of oil and oil research. However, is increasing the number of world discoveries of oil where magmatic rocks constitute hydrocarbon reservoirs. The study of these reservoirs known as unconventional has shown the importance of magmatic events in sedimentary basins for the exploration of hydrocarbons.

In this article we will show how tools like Hardledge® and Strataledge®, helps in the routine work of exploration and production of complex hydrocarbon reservoirs

Igneous-sedimentary oil system

When we talk about oil reservoirs, we soon think in sedimentary rocks, mainly sandstones and carbonates. These rocks are commonly associated with better hydrocarbon reservoirs and are called conventional reservoirs.

However, igneous rock may also constitute a reservoir. The igneous-sedimentary oil systems are unconventional, mixed systems in which one or more essential elements or processes involved are related to magmatic events (Figure 1).

Example of occurrence of magmatic rocks
Figure 1. Example of occurrence of magmatic rocks associated with sedimentary rocks in the Argentinian Neuquén basin. This reservoir of fractured igneous intrusion holds 25 million barrels of recoverable oil per field, and are characterized by rapid initial production rates of up to 10,000 barrels/day. (Source: Senger et al., 2017)

It turns out that for a long time the presence of intrusions and extrusions of magmatic material in the basin was seen only as unfavorable in terms of exploration. The magma was responsible for destroying the organic matter and the oil previously generated, besides obliterating the pores of the rocks-reservoirs.

Recent studies show that in fact volcanic complexes impact the oil systems in a variety of ways, not necessarily destroying or obliterating their viability, but favoring the formation of conventional or unconventional reservoirs.

The importance of knowledge of the magmatic rocks in the sedimentary basins as potential reservoir rocks of hydrocarbons has been strongly discussed in the last years, due to the numerous exploratory discoveries (Figure 2) involving these rocks as carriers of hydrocarbons.

The discoveries of reservoir with related volcanic rocks around the world
Figure 2. In green, the discoveries of reservoir with related volcanic rocks around the world. (Source: Senger et al., 2017).

The petroleum system comprises five main elements: (1) a source rock subject, over sufficient time, to conditions leading to hydrocarbon generation; (2) pathways for the generated hydrocarbons to be expelled from the source rock and move to a reservoir rock; (3) a porous and permeable rock to serve as a reservoir for the hydrocarbons, and (4) an enclosing structure for trap the oil with (5) low permeability extremities for seal the reservoir.

The magmatic events may affect any of these five elements, favoring:

1) Hydrocarbon generation

Unlike of conventional oil systems, where the formation of oil and gas is due to the heat supply generated by the subsidence of the basin, in atypical petroleum systems, magma intrusions are responsible for the increase in temperature in the system.
The emanated heat around of the magmatic intrusion causes the vaporization of the water contained in the pores of the embedding rock, resulting in the dehydration and decarbonization and consequent maturation of the organic matter. The intrusions commonly occur as (Figure 3):

(1) layer-parallel and transgressive sills
(2) saucer-shaped intrusions
(3) layer-discordant sub-vertical dykes
(4) localized volcanic centers

Cross section through a volcanic basin
Figure 3. Cross section through a volcanic basin highlighting some of the key terminology and relationships of the igneous rocks with the host basin (link)

The most common types of igneous intrusions in sedimentary basins are dikes and sills. Dikes are discordant structures, usually perpendicular or inclined to intruded bedding. Sills are concordant structures, parallel or subparallel to the sedimentary layers. Both dykes and sills form contact metamorphic aureoles caused by localized heating of the adjacent host rock (Figure 4).

Examples of sills and dikes infiltrated among the oldest layers
Figure 4. Examples of sills and dikes infiltrated among the oldest layers (link).

The extent of the thermal effect on the oil system of a sedimentary basin depends on factors such as mineralogy of the embedding rocks, thickness and temperature of the intrusive, depth of intrusion, composition of the available fluids, the time and duration of the magmatic event, among others.

The effect of an intrusion on the embedding rock is equivalent to the thickness of the intrusive body. As we move away from the igneous body there is a progressive decrease in the levels of organic carbon and expansive minerals. Multiple intrusions have this potentiated effect. In addition, the greater the depth the greater the transmitted heat and the larger the effect dimensions.

The distinct geophysical properties (density and resistivity) between the intrusions and the host rocks facilitate the identification of the igneous rock through seismic profiles, for example. Nonetheless, many thin sills fall below the seismic resolution. In addition, the complex and often discordant geometry of igneous bodies presents significant challenges to imaging both in seismic data and in resistivity mapping.

The recognized of igneous bodies and your registration in the fieldwork provides the necessary for link the geophysical measurements to exposed igneous intrusions where critical details. Strataledge® software has a large taxonomy of igneous lithological units that facilitate the process of descriptions of outcrops and cores and allow the integration with geophysical profiles, which help in identification and location of igneous bodies in the basin.

2) Oil migration

The oil migration process can occur in three stages: 1) Primary migration: oil is expelled from the generating rock to the carrier bed; 2) Secondary migration: the oil migrates inside the carrier rock into the trap; 3) Tertiary migration: any movement of the oil after your trapping.

An igneous intrusion can function both as a conduit for hydrocarbon migration and as a barrier to the flow of fluids. If the intrusion present faults and has good permeability, it will act as a migration route. If it is mineralized and impermeable, it will form a structural trap preventing the passage of fluids.

Knowledge of the parameters that control magmatic intrusions generates important information about the paths of fluid migration. For example:

a) Factors such as composition, cooling rate, depths and permeability of h ost rock influence the nature of the fracture network.
b) Structurally complex zones such dike-sills junctions, sill inflection points and intrusion-host rock interfaces are typical of zones with good permeability.
c) The hydrothermal fluids activity, post-emplacement diagenetic processes and tectonism give information if the intrusions are open and interconnected or cemented and closed.
d) Heterogeneities between magmatic intrusion and sedimentary rock can be an important migration route.
e) The geometry of the igneous plumbing system will also influence the migration routes.

3) Storage of hydrocarbons

For a rock to be considered as a reservoir, it must have an appropriate combination of porosity and permeability values that enable the accumulation of hydrocarbons. It’s know that the primary matrix porosity and permeability of igneous rocks is generally very low.

How can they be good oil storage? In igneous rock, these significant values of porosity and permeability may develop owing to fracturing, zones with vesicles, and in hydrothermally altered zones.

Often the igneous body may present these features, but its effectiveness varies according to the lithological facies. The fracture system must be well developed and interconnected, the volume of vesicles must be considerable and the degree of alteration, associated with microfracture.

The vesicles (Figure 5A), for example, act as pores and are concentrated as the top and base of spills and originate from the dissolution of vesicular material. It is common during the cooling of the lavas to form microfracture (Figure 5B) by thermal contraction that form a network joining vesicles that assist in the dissolution of the filling material and allow the entry of oil.

In addition, highly weathering processes in these zones and fluid circulation contribute to the increase of microporosity, creating channels and spaces for hydrocarbon migration and trapping.

 Vesicular porosity
Figure 5. (A) Vesicular porosity (B) Vesicular porosity with microfractures (red arrows) Reis et al., 2014.

Intemperic processes can cause compositional changes and variation in the characteristics of volcanic rocks, increasing their permeability and porosity values. Altered samples develop micropores as a consequence of the predominance of clay minerals and mineral alterations of feldspar and volcanic glass.

On the other hand, solid and impermeable rocks as extrusive manifestations can act as effective lateral sealants or migration barriers, allowing the accumulation of hydrocarbons generated in the adjacent sediments. Sills act as vertical seals, while dikes act as side seals.

Recognize the geometry of the igneous bodies and the structural elements that were induced by magmatic intrusions and are present in the embedding is of paramount importance for the understanding of the reservoir.

Intrusions of igneous rocks in sedimentary basins may be useful as stratigraphic landmarks and indicators of turbidity sedimentation, as is the case of layers of bentonite derived from volcanic ash. They can also generate secondary tension fields that can deform the embedding sedimentary rock and generate traps for imprisoning oil. Or contribute as an extra source of heat for oil generation in shallow and cold basins.

Exploration of hydrocarbons in unconventional basins

We saw that the magmatic events may affect the basin and favor the formation of reservoir oil. On the whole, volcanism, tectonic movements, weathering, leaching and fluids are key factors and geological actions for the formation and development of reservoir spaces in volcanic rocks.

The presence of rock types derived from volcanism and/or affected by post-volcanic re-deposition may lead to complex lithologies, with complex diagenetic overprints at the reservoir level. Diagenesis and diagenetic evolution of altered volcanic materials have a profound effect on the pore evolution of hydrocarbon reservoirs. Hardledge® is an essential software for the systematic petrographic analysis of igneous and metamorphic rocks. It enables to do a detailed petrographic description and interpretation. The descriptions easy import in Strataledge® for integrated visualization and analysis with multiple data sources.

Most of the time the reservoirs are offshore, at great depths, making it difficult to understand and the processes that led to the accumulation of hydrocarbons in the spills. It is necessary to develop similar models that allow the knowledge of the permoporous system and the consequent better exploitation of these reserves.


Senger, K., Millett, J., Planke, S., Ogata, K., Eide, C. H., Festoy, M., Galland, O. and Jerram, D. A. 2017. Effects of igneous intrusions on the petroleum system: a review. First break, Volume 35, p. 1-10.

Reis, G.S., Mizusaki, A.M., Roisenberg, A. and Rubert, R.R., 2014. Formação Serra Geral (Cretáceo da Bacia do Paraná): um análogo para os reservatórios ígneo-básicos da margem continental brasileira. Pesquisas em Geociências, 41 (2): 155-168.


  • Sabrina Danni Altenhofen – Endeeper

Guide to Value Creation from Geological Data in Oil Exploration

Technology is a strong ally for the acquisition of geological data. With the huge demand for fast and reliable results, changes and updates in the tools used by geologists are necessary. Investing in the generation of relevant data for aiding the interpretation and decision making during the exploration for oil reservoirs is critical for reducing the costs of these activities.

In this guide, you will understand the importance of the analysis of cores, outcrops and petrographic thin sections for geological studies applied to reservoir exploration, and you will learn why software are essential tools for digital geology.

Importance of the core/outcrop descriptions for geological studies

The consolidated rocks that you describe in cores or outcrops are product of transport and depositional processes that are recorded in the form of sedimentary structures and textures. To describe cores/outcrops is understand the processes of formation of the rocks. The more detailed and complete the descriptions, the more precise will be the interpretation of the source areas, transportation processes and depositional environment.

The description is important for the knowledge acquisition and the immediate visual perception of the rock package. With a good description, it is possible to interpret the succession (facies distribution and sedimentary cycles) of the sediments deposition events and to perform correlations with equivalent beds from other areas, in search of understanding the stratigraphic framework of the basin.

This descriptive and analytical work can be better performed with the use of specialized software, such as Strataledge, which helps the geologists in their routine data acquisition tasks. In the section below, we explore the advantages of using software in relation to paper for analyzing cores and outcrops.

Efficient analysis of cores and outcrops with Strataledge

Rock outcrops (Fig. 1) are surface expression of the internal and external dynamics of the Earth.

Outcrop of stratified sedimentary rocks
Figure 1. Outcrop of stratified sedimentary rocks. Source: link.

The geologist usually uses a booklet or clipboard to draw a stratigraphic columnar log (Fig. 2). Information such as location, reference coordinates, date and time are recorded. Information relevant to the field work are also normally recorded, such as stratigraphic units, age, and structural observations. The work scale to be used must be selected according to the total size of the outcrop and the desired detail.

Example of stratigraphic log sheet
Figure 2. Example of stratigraphic log sheet.

Core description is performed in the laboratory on sequential log description sheets. Cores collected during drilling are placed in properly identified boxes following the stratigraphic stacking order (Fig. 3).

Core boxes.
Figure 3. Core boxes.

The description is usually made from base to top, which reflects the order of deposition. The geologists describe the following aspects:

  1. Rock types
  2. Minerals and other constituents (e.g., fossils)
  3. Textures
  4. Structures indicating processes of deposition, deformation or other alterations
  5.  Thickness layers and types of contacts among them

The geologists can interpret depositional facies, identifying them through a lithofacies code. After all this information has been recorded, the rock packages are stacked and the vertical succession assembled. From this, the geologist may ask the following questions:

  1. Is there a stacking pattern of facies?
  2. The pattern repeats itself? Is there cyclicity?
  3. Are there interruptions or abrupt changes in the sedimentation process?

These questions define boundary surfaces that delimit facies associations. Each association of facies presents a distinct genetic meaning, allowing the determination of a depositional model.

If samples or fossils are collected, their exact location must be noted. Also, photos are recorded for inclusion in the description and in reports.

All the data is conventionally recorded on paper by the geologists and digitalized after description. This process requires a considerable amount of time. Fortunately, there are faster ways of generating data from core and outcrops.

Through Strataledge® (Fig. 4), the descriptions are performed directly in digital format, reducing drastically the acquisition time, as well as in a clear and organized way.

The system allows the detailed and systematic description of cores and sediments, providing quick access to all the required features for data acquisition and recording of sequential data.

Strataledge Screenshot – Digital Core Data.
Figure 4. Strataledge Screenshot – Digital Core Data.

Strataledge® has a broad taxonomy of lithological units, sedimentary and deformation structures and petrological characteristics. All of this information, necessary to obtain a complete core description, has standardized nomenclature and is efficiently recorded under a clean and organized interface.

The use of Strataledge® optimizes and considerably reduces the time normally spent during description work, as well as allows reliable and quality records that will be compatible with other descriptions made by other professionals.

Descriptions recorded through Strataledge® are easily exported in a wide variety of formats, such as LAS, CSV and SVG (Scalable Vector Graphics).

In addition to the macroscopic description of cores and outcrops, petrography is also used to characterize rocks on a microscopic scale. See below…

The importance of petrographic characterization for geological studies

Petrography is a key tool for understanding the origin and evolution of rocks. Through the petrographic analysis of thin section, it is possible to determine the processes of sediments deposition and their transformation into rocks during diagenesis, including the precipitation of authigenic minerals, compaction, dissolution, replacement, and other mineralogical transformations. Their description and interpretation are important for the characterization of the quality of rocks as petroleum reservoirs.

Software for enhancing, systematizing and facilitating the petrographic description are important tools for the exploration and production. Know the Petroledge® system in the next section.

Description of petrographic thin sections with Petroledge® Software

Systematic petrographic analysis is performed using polarized light microscopes as shown in Figure 5. Quantitative analysis can be performed with use of a mechanical device (chariot) that allows moving the thin section along specific step over the microscope rotating table, recording the composition at each step, or modal point-counting (Fig. 5).

The modal analysis is performed according to transverses perpendicular to the lamination or orientation of the grains. This step is selected according the rock texture. At each step, the constituent located under the crosslines of the microscope eyepiece is recorded. This procedure is usually repeated to a total of 300 counted points to ensure a statistically reliable quantification.

Workstation for digital systematic petrography, with a petrographic polarized light microscope and mechanical chariot coupled (left).
Figure 5. Workstation for digital systematic petrography, with a petrographic polarized light microscope and mechanical chariot coupled (left).

Currently, the conventional mechanical devices for point-counting can be replaced for Stageledge®, a high-precision digital stage (Fig. 6). Compatible with the main polarized microscope models available on the market, this automatic stage moves the thin section with precision recording the composition at each point. A compositional map can be generated, and the percentages of the constituents are automatically calculated. The recorded points can be consulted at any time, simply clicking on the desired point so that stage is automatically moves back to the exact location of the chosen point.

Digital Point Counter - Stageledge®.
Figure 6. Digital Point Counter – Stageledge®.

The systematic description of thin sections is better performed with use of the Petroledge® system, including the description of texture, primary composition, diagenetic composition and sequence, pore types and classification.

When starting a petrographic analysis with Petroledge®, the identification window (Fig. 7) is used to insert well / outcrop name, depth, core and box numbers, basin name, stratigraphic unit, field name, country, state, and other location data. In addition, name of the petrographer, institution, purpose of the description and a brief summary of the thin section description, addressing the main aspects observed during petrography. This summary is important to facilitate future consultations.

Thin section identification screen of Petroledge®
Figure 7. Thin section identification screen of Petroledge®

Petrographic description with the Petroledge® software is performed in a logical order and follows taxonomy. This allows the program to recognize and process the recorded data and automatically generate compositional and textural classifications, recognize tectonic provenance modes and interpret diagenetic environments.

In Petroledge® microscopic description screen (Fig. 8), it is possible to describe textural, fabric and structural aspects.

The main aspects that can be recorded include a wide range of depositional, deformation and diagenetic structures, grain size and shape, orientation, support and other fabric aspects.

The types and proportion of intergranular contacts are used to calculate the packing index, relative to the degree of compaction.

Microscopic structure, texture and fabric description screen in Petroledge®.
Figure 8. Microscopic structure, texture and fabric description screen in Petroledge®.

The next description step is performed in the compositional description window (Fig. 9). The description and quantification of primary and diagenetic constituents and pore types is carried out in an interface that allows recording in detail important aspects for reservoir quality characterization. Primary constituents are described for types, locations, and modifications. For diagenetic constituents, a detailed description allows recording habit, location, and paragenetic relations with other constituents or with porosity.

Example of the compositional description screen in Petroledge®, showing the degree of information detail allowed by the system.
Figure 9. Example of the compositional description screen in Petroledge®, showing the degree of information detail allowed by the system.

This characterization is necessary in order to define the control of diagenetic constituents on the porosity and permeability of the reservoirs. Pore types are described relative to location and modifying processes, paragenetic relations, constituent types and location.

With proper quantitative description, the rocks can be automatically classified (Fig. 10) in a series of systems, including Folk (1968) and McBride (1982), for siliciclastic rocks, Dunham, Embry and Klovan (1962), Gabrau, Brankamp, Powers and Folk (1958) and Wright (1992), for carbonate rocks. It is also possible to define the tectonic provenance mode of the sediments according Dickinson (1985).

The automatic classification of groups of thin sections can be easily performed (Fig. 10).

Example of thin section groups of classification in Petroledge.
Figure 10. Example of thin section groups of classification in Petroledge.

Importance of systematic photomicrographic record for geological studies

Photomicrographic documentation is important to record the main compositional, textural, and structural features of the analyzed rocks.

The photomicrographs illustrate and support interpretation and data integration. Acquisition is performed through digital cameras attached to the microscopes.

A systematic digital catalog of photomicrographs is of great value for geological studies. See the case of RockViewer®.

RockViewer® system for photomicrograph organization and support to petrographic analysis

Data organization is a key factor for exploration and production research. Through the use of the RockViewer® system, a photomicrographic database can be created, allowing future queries in a quick and easy way. In addition, this database acts as a support for petrographic features identification and characterization, aiding in the process of thin sections description.

With the RockViewer® image editor (Fig. 11) it is possible to record aspects of the photomicrographs that can be easily consulted. The user can highlight important features in the image with shapes like arrows, outlines, letters, among others. All features are recorded through the Petroledge® taxonomy, allowing easy recovery of specific features. Other information, such as thin section identification (name of basin, stratigraphic unit, etc.) can also be added. Additional observations can be also added as notes.

Image editor screen in RockViewer®.
Figure 11. Image editor screen in RockViewer®.

All added images can be easily accessed (Fig. 12) and searched in a hierarchical order, where the terminology of geological features is available in four separate lists: rock type, concept, attribute and value. According to the items selected in each list, the concepts are automatically filtered.

Search screen in the RockViewer® image catalog.
Figure 12. Search screen in the RockViewer® image catalog.

By selecting the images from the result set, all of the information described by the user will appear, highlighting the searched terms (Fig. 13).

Detail of an annotated image created in RockViewer®.
Figure 13. Detail of an annotated image created in RockViewer®.

Key for creating value from geological data: Integrated data analysis

The systematic acquisition and organization of digital data allow efficient integration and interpretation.

Strataledge® software allows the complete integration of cores and outcrops descriptions with or detail photos, petrographic descriptions, geophysical logs and other media (Fig. 14).

Example of a study performed in Strataledge® integrating core descriptions, geophysical logs, petrographic data and core photos.
Figure 14. Example of a study performed in Strataledge® integrating core descriptions, geophysical logs, petrographic data and core photos.

A typical integration example involves the integration of petrographic data with geophysical logs of physical properties of the rocks, such as radioactivity, resistivity, density, acoustic wave propagation and others.

This continuous record generated by the geophysical logs allows to evaluate the petrophysical or geometric characteristics of the geological formations traversed by the well.

With the use of Strataledge®, geophysical profiles can be effectively integrated with core descriptions, photos and petrographic descriptions, facilitating the integrated data analysis.

Furthermore, it is possible to easily make stratigraphic correlations (Fig. 15), important for the determination of lateral rocks continuity, or the spatial equivalence between several lithologic units in subsurface. At this step, the importance of the integration of all the data previously generated is highlighted.

Example of datum created through the stratigraphic correlation in Strataledge®.
Figure 15. Example of datum created through the stratigraphic correlation in Strataledge®.

The search for hydrocarbon reservoirs during exploration should integrate organized geological information to aid decision making and reduce the risks associated with the exploratory activities.

All of the steps discussed in this guide be incorporated in the routine work of exploration, but this often take a long time due to the lack of integration between the tools. At each step, important data is generated, which must be integrated and interpreted. The modern exploration and reservoir professionals must rely on intelligent and innovative tools that, in addition to speeding up systematic description, and storing the data in an organized way, also aid the integration and interpretation.

Software developed for knowledge management such as Strataledge® and Petroledge® guide and standardize the description process through the combination of ontologies and taxonomies. This combination enables the construction of valuable geological databases, simplifies information quality control and enables the automatic extraction of knowledge using artificial intelligence. Simplifying tasks reduces data analysis time and ensures decreased exploration costs and risks.

Visit for more information about Endeeper oil exploration software


  • Sabrina Danni Altenhofen – Endeeper
  • Elias Cembrani da Rocha – Endeeper
  • Eduardo de Castro – Endeeper

Digital Petrography – Fundamental Tool for Understanding Carbonate Reservoirs of Campos Basin

Learn why petrographic characterization is a fundamental tool for understanding Carbonate Reservoirs of the Campos Basin.

The Challenge

Campos basin is the most prolific Brazilian basin. Hydrocarbons are sourced mainly from lacustrine rift section, which also contains important carbonate reservoir rocks. Diagenetic processes strongly influenced the porosity and permeability of these lacustrine carbonates. Understanding the controls and patterns of diagenesis is fundamental for the construction of geologically realistic and effective models for the exploration and production of these reservoirs.

Petroledge Petrography Carbonate

The Solution: Systematic Petrography using Petroledge®

A systematic petrographic study of the rift carbonate reservoirs and associated lithologies was developed in central Campos Basin with use of the Petroledge® software. The petrographic characterization, which comprised all major aspects of depositional structures, textures, primary composition and diagenesis, helped to define the depositional and post-depositional conditions of the succession, as well as the main controls on the reservoirs quality. The Petroledge® system has unique features, designed to facilitate and support petrographic description, as well as automated classifications and multi-format reporting, ensuring efficient and rapid data analysis. Systematic acquisition and processing of petrographic data and information provided by the Petroledge® software allows an optimized use of petrographic information for understanding of the distribution of porosity and permeability.

Geological Context

The origin of the Campos Basin is linked to the initial stage of separation of the African and South American continental blocks in the Early Cretaceous. The initial phase of basin evolution was characterized by rift half-grabens, where fluvial and lacustrine sediments were deposited. The vertical succession analyzed in this study interval is composed of a siliciclastic and volcanoclastic basal section, covered by a complex succession of ooidal stevensite arenites, bioclastic grainstones and rudstones (which includes the reservoirs), and mudrocks.


The integration of the results of the petrography with seismic, stratigraphic and sedimentological information allowed to conclude that:

  • The analyzed rocks are composed of extrabasinal sediments (siliciclastic and volcanoclastic grains and siliciclastic mud) and mainly intrabasinal carbonate and stevensite constituents.
  • The main carbonate rocks correspond to ostracod grainstones and bivalve rudstones, commonly known as “coquinas”, which correspond to the main reservoirs.
  • There is widespread mixing of the bivalve bioclasts with stevensite ooids and peloids. As the precipitation of stevensite occurs only at highly alkaline conditions (pH> 10, high concentration of Mg and Si), which would be intolerable by the bivalves, such mixing would be possible only through re-sedimentation. The distribution of the seismic facies corresponding to the bioclastic deposits and their massive structure indicate that this re-sedimentation took place from different shallow water environments to deep lacustrine settings, though gravitational flows.
  • The mixing of bioclastic and stevensitic constituents has important implications for the quality of the rift reservoirs. Hybrid deposits with significant mixing are commonly strongly cemented, while rudstones with minor or no mixing with stevensitic grains show better preservation of interparticle porosity. These best reservoirs would correspond either to bioclastic deposits in their in situ shallow sites, or to re-sedimented deposits that were not mixed with stevensite sediments.

The systematic petrography of the bioclastic carbonate reservoirs of Campos Basin allowed by the Petroledge® software was essential for the understanding of depositional and post-depositional conditions of the rift succession, as well as of the main controls on the quality of the reservoirs.


  • Sabrina Danni Altenhofen – Endeeper

Ontologies and data models for petroleum exploration

This post shows how ontologies can play a central role in data integration for petroleum exploration.

Petroleum exploration and production rest on reservoir models that integrate a large set of data of various kinds. The common backbone of these data is the object of the modeling itself: the reservoir and the geological properties attached to it. Each category of professionals involved in the reservoir study views this reality according to some specific field of knowledge. These specialists thus generate various sets of data, each resting on a different conceptualization of one same object: the petroleum prospect. The resulting data models can be efficient in attending a particular application, but they are hardly interoperable and thus difficult to use in federate software environments. In view of this situation, petroleum exploration appears to be a domain rich in challenges related to conceptual modeling and data integration, in which ontologies can play a central role.

Ontology definition

Ontology is a branch of Philosophy that studies the nature of existent beings and their mutual relationships. In Computer Science, the term ontology/ontologies has been used to designate an artifact (a file, a description, a representation) that formally describes, in a computer language, a set of concepts, whose meaning is shared by a community of practitioners. Significant progress was made in the field of ontologies in the late 90’s, when Nicola Guarino analyzed the various meanings in which the word ontology was being used (Guarino 1998). He insisted on the idea that an ontology is, primarily, a logical theory accounting for the intended meaning of the formal vocabulary utilized by a community for naming the elements of its domain. Guarino introduced a few meta‐properties based on philosophical notions, such as identity, unity, rigidity, and dependence (Guarino & Welty 2000), which greatly help to clarify the meaning of the concepts that are currently expressed by means of domain ontologies in the various fields. We intend to demonstrate here, by a gentle introduction of two of these metaproperties – rigidity and dependence ‐ that analyzing information through the view of ontological metaproperties, as proposed by Guarino, can be helpful for reducing both the complexity and the ambiguity of data models.

The use of ontological metaproperties in modeling

The first useful ontological notion is essence. According to (Guarino & Welty 2004), a property attached to an entity is essential to this entity if it must hold for it in every possible world. For example, being crystalline is an essential property for a mineral but it is not for a gemstone, since we can produce gemstone from non‐crystalline material, like amber. When a property is essential for every instance that can exhibit it, we say that this property is rigid. The notion of property, in Logic, refers to every predicate that can be applied to a given instance, like “being a horse”, “being a mineral” or “having a brain”. In our example, “being crystalline” is essential for minerals, but not for other substances, like glass, so it is not a rigid property. Considering another example, a human being is an instance of the concept person and a human being is a person along all his life (and even after). Then the quality of “being a person” is rigid since there is no instance of human being that can stop being a person. Conversely, being a student is not a rigid property, since someone can stop being a student without stopping existing. A piece of mineral cannot stop being a mineral, but an entity which we consider being a gemstone, has not been a gemstone all along its existence since it was not one before having been cut and polished in order to be used in jewelry. Student and gemstone are defined by anti‐rigid properties that define roles, like a student related to a person, or phases, like gemstones related to some mineral piece.

The notions of essence and rigidity help in identifying the concepts in the domain that provide the identity to individuals and can be tracked in the models. It thus allows one to identify vocabulary practices that may cause ambiguity like denominating instances of a domain according to anti‐rigid properties and building models over anti‐rigid concepts. For example, naming a person as a “client”, a geographic area as a “prospect”, a geological unit as an “economic target” hardly help in producing long term integrable models.
In the field of data models, considering essential properties allows one to correctly identify entities and to produce a more precise representation, which facilitates further integration and interoperability. We will analyze here a simple example related to petroleum exploration: the modeling of the entity reservoir.

In the context of petroleum exploration, a reservoir is a volume inside a prospect, which may contain petroleum and water. For modeling it, we must examine whether the property of “being a reservoir” is rigid or not. In other words, we should decide whether some entity called “reservoir” may stop being a reservoir and still exist. The answer strongly depends on the modeler’s conceptualization of a reservoir. Some geologists may simply define a reservoir as a portion of rock having high porosity/permeability. This definition is rooted in some intrinsic properties of the entity (porosity and permeability) that cannot be lost . In this case, “being a reservoir” is a rigid property. This first conceptualization will produce the model showed in the Figure 1(a).

Alternative models for the entity reservoir based on intrinsic essential properties (a) or on external dependence (b).
Figure 1 – Alternative models for the entity reservoir based on intrinsic essential properties (a) or on external dependence (b).

However, some other geologists will consider that a portion of rock with high porosity and permeability is not a reservoir until its voids actually contain petroleum or water. This second definition implies that an instance will stop being a reservoir if it stops having water or petroleum inside its empty voids. If a reservoir is exposed to air, it will lose its content of petroleum or water, but the volume of rock to which it corresponds will not disappear. But, according to our second model definition, it will stop being a reservoir. The property of being a reservoir in this second model is anti‐rigid. It is just a role of some existent portion of rock that should be considered as an entity of another concept, such as Rock body, and modeled in this way in the data model.

As shown in Figure 1 (b), this second model requires the modeling of a second entity, petroleum or water, which specifies the relational dependence that affects the instance of the reservoir that we consider. Any instance of an anti‐rigid role concept has a relational dependence on some instance of another concept. It can exist only if the relationship exists. For example, a “student” cannot be a student if there does not exist some school or university in which he/she is registered. In our second model, an instance of reservoir cannot exist if there does exist a fluid (water or petroleum) inside its voids.

Deciding what is the rigid entity that provides identity to the several roles that an instance can assume is a central task in producing precise and efficient data models. The taxonomic (or hierarchical) structures that are defined, determine the subsumption relations that can be established between the various entities. Entities defined by anti‐rigid properties cannot subsume entities (i.e. be the super class of) rigid ones (Guizzardi & Wagner 2005). Let us consider the schema shown in figure 2, which intends to model the variety of reservoirs that are explored in a petroleum company.

Wrong use of the subsume relation.
Figure 2 – Wrong use of the subsume relation.

The model shown in figure 2 is wrong because the class Reservoir cannot subsume the subclasses Sandstone and Fractured schist. According to the schema of figure 2, the reason is that, the extensions (instances) of Sandstone and Fractured schist should be also extensions of Reservoir but this is not right since these rocks do not always constitute reservoirs. According to the design pattern proposed Guizzardi in (Guizzardi & Wagner, 2005) for dealing with such cases, we propose a better model on the schema of Figure 3. In this schema, the entities marked in grey are defined by rigid properties.

Conceptual modeling based on ontology properties - ontologies.
Figure 3 – Conceptual modeling based on ontology properties.

Advantages on ontological analysis

Ontological choices are not only an academic issue related to different modeling options. These choices have practical consequences for model usage and data consultation. In the example, the option of considering the reservoir entity as dependent of the entity fluid allows to create instances of fluid types or occurrences and to associate them to a particular instance of reservoirs. The first modeling option doesn´t allow this usage. Moreover, the model ambiguity can be reduced when the meaning of the represented entities is made explicit. This avoids that the same vocabulary be used to refer to two or more concepts that modelers or users consider being distinct. We additionally claim that providing a common framework based on essential entities allows reducing the number of entities and complexity of the resulting model. Other ontological metaproperties require a better analysis in conceptual modeling activity. Especially in Petroleum Geology, properties like identity and unity can help in defining what exactly are the entities of reality that are being modeled in the database and also provide a good support to integrate models in the several scales of analysis (microscopic, well, reservoir, basin scales) into the petroleum chain. These metaproperties will be object of a further discussion.

Example of ontology for rock data management: Strataledge.

Acknowledgement: This article about ontologies is a corrected version of those published on Foundation Journal of the Professional Petroleum Data Management Association, Vol 3, Issue 1, pp.18‐19, 2016. We are grateful to Nicola Guarino by the improvement in conceptual issues after the publication.

Authors: Abel, M.(1); Perrin, M.(2) ; Carbonera, J.L. (1); Garcia, L. (1)
(1)Informatics Institute – Universidade Federal do Rio Grande do Sul, Brazil
(2) Geosiris, France.


  • Abel M., Perrin M. & Carbonera J. (2015). Ontological analysis for information integration in geomodeling. Earth Science Informatics, 8, 21‐36. Springer.
  • Guarino, N., ed. (1998), Formal Ontology in Information Systems. Proceedings of FOIS’98, Trento, Italy, 6‐8 June 1998, 3‐15. IOS Press,
  • Guarino, N., ed. (1998), Formal Ontology in Information Systems. Proceedings of FOIS’98, Trento, Italy, 6‐8 June 1998. Amsterdam, IOS Press, pp. 3‐15.
  • Guarino N. & Welty C. (2000). “A Formal Ontology of Properties”. In: The ECAI‐2000 Workshop on Applications of Ontologies and Problem‐Solving Methods. IOS Press
  • Guarino N. & Welty C.A. (2004). “An overview of OntoClean”. In: Handbook of Ontologies (eds. Staab S & Studer R). pp. 151‐171. Springer
  • Guizzardi G. & Wagner G. (2005). Some applications of a unified foundational ontology in business modeling. Ontologies and Business Systems Analysis, 345‐367. IGI Global.

The Power of Ontologies for Knowledge Management

Ontologies, as a tool for knowledge management, contribute to organize and make explicit the knowledge modeller intentions. Let’s take a look.

Tacit knowledge, as defined by Nonaka and Takeuchi, is the type of knowledge residing in people’s minds, while explicit knowledge is the type of knowledge not residing in people’s minds but in structured documents and central repositories of knowledge, for example [Nonaka, 2008] . A knowledge management project is an effort developed by companies in order to collect the tacit knowledge, which is distributed among the employees, and turn it into a company asset as explicit knowledge [Davemport, 1998].

Ontologies have their origin in the Philosophy field and refer to a system of categories that commit to a certain vision of the world [Guarino, 1998]. They are applied in computer science in order to capture domain knowledge and to explicit the assumptions that define the knowledge’s intended meaning [Guarino, 1998]. The picture below shows a simple example where two people use the same vocabulary to communicate. They use the same vocabulary representation (the word “Orange”), but they refer to different meanings: one talks about the color, while the other talks about the fruit. This is a common communication problem when exchanging information using documents or when integrating software systems. Applying ontologies to capture domain knowledge allows the knowledge engineer, responsible for a knowledge management project, to identify the company’s knowledge, structure it, and store it independently from the people who use that knowledge on their daily activities.

Ambiguity - Knowledge Management

The problem of ambiguity: two people talking to each other and using the same vocabulary, while the intended meanings are different.

Ontologies are currently being applied to the petroleum exploration chain to make it possible to capture the semantics behind the representations defined for geological reservoir models. They also provide support for data interoperability among the software systems applied to the petroleum exploration chain. Endeeper develops a family of ontology-based software systems. Petrographic software systems like Petroledge, Hardledge, and RockViewer are based on the Petrographic Ontology, which was created by [Abel, 2001]. Strataledge is based on the Stratigraphic Ontology, which was created by [Lorenzatti, 2010]. Both ontologies are maintained by Endeeper and are in constant evolution in order to better capture the geological knowledge applied by companies to describe rocks and evaluate the quality of petroleum reservoirs.


  • Abel, M. The study of expertise in Sedimentary Petrography and its significance for knowledge engineering (in Portuguese). (Doctoral Thesis ). Informatics Institute Federal University of Rio Grande do Sul, Porto Alegre, 2001. 239 p.
  • Davenport, Thomas H., David W. De Long, and Michael C. Beers. “Successful knowledge management projects.” MIT Sloan Management Review 39.2 (1998): 43.
  • Guarino, N. Formal Ontology in Information Systems In: N. GUARINO (Ed.). Formal Ontology in Information Systems, FOIS’98. Trento, Italy: IO Press, 1998. Formal Ontology in Information Systems p.6-8 June 1998.
  • Lorenzatti, A. Ontology for Imagistic Domains: combining textual and pictorial primitives (in Portuguese). (Master Dissertation). Informatics Institute Federal University of Rio Grande do Sul, Porto Alegre, 2010.
  • Nonaka, Ikujiro. The knowledge-creating company. Harvard Business Review Press, 2008.

Petrography by Petroledge: Campos Basin success case

This post presents an example of project that uses Petroledge for understanding the Campos Basin rift reservoirs using petrography.


Digital Petrography by Petroledge and Stageledge
Digital Petrography by Petroledge and Stageledge

An integrated, seismic-stratigraphic-sedimentological-petrographic project, developed by Brazil’s Rio Grande do Sul Federal University for BG Group, shed new light on the depositional and diagenetic controls on the origin, geometry, distribution, quality and heterogeneities of Campos Basin rift reservoirs and associated lithologies.

The use of the PETROLEDGE® system was vital for the systematic acquisition, storage and processing of petrographic data from the complex and unconventional rocks that constitute the pre-salt, rift section of the basin. “Our understanding of the dominantly intrabasinal nature of the rift sediments and their conspicuous re-deposition by gravitational processes was enhanced by the detailed, yet flexible petrographic descriptions allowed by PETROLEDGE®”, says Dr. Karin Goldberg, head of UFRGS project. Campos rift sediments are essentially constituted by complex mixtures of carbonate bioclasts, siliciclastic and volcaniclastic particles, and stevensite (Mg-smectite) ooids and peloids.

Endeeper PETROLEDGE® system is being used globally by a series of universities and exploration companies, which are taking advantage of the systematic and efficient acquisition and processing of petrographic information generated by the system.

Tools for RESQML data management

RESQML™, according to Energistics, is an industry initiative to provide open, non-proprietary data exchange standards for reservoir characterization, earth and reservoir models.

We have recently released an article that describes the public domain tools that the RESQML community is offering for allowing developers and end users to validate RESQML EPC instances written and read by RESQML users: software vendors (Paradigm, Schlumberger, and others), petroleum companies (Total, Shell, Chevron) owning proprietary products or international research centres.

Endeeper team can help oil and gas companies to evaluate the adoption of RESQML standard.

Click here to get more information about the RESQML tools article.