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Learn petrophysics by practicing with log interpretation examples


Once you have any GeolOil license (even a trial license), you may acquire our affordable server access service to an extra data set examples of fully interpreted well logs. The server access will last until the life term of your license.


Even if you regularly use another commercial petrophysical software, it is a hands-on valuable tool to understand petrophysical concepts, interpretation and computation work-flows. Learn how to compute porosity from different sources, matrix density, shaliness, mineral proportions solvers, scripting, and produce nice log displays. The data collection is continuously growing with key cases from naturally fractured reservoirs, clastic reservoirs, carbonate reservoirs, unconventional tight, and shale oil reservoirs.



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LEARN SET: Life-time access to extra data set of examples. Requires a GeolOil license

GeolOil Sofware server cloud data access
Lease term Price
Life-time
Only $ 36
Extra example logs data set

HIGHLY RECOMMENDED: This server service of optional access to the log examples extra data set is continuously updated and growing. It contains fully interpreted and processed logs with nice displays, equations work-flows, scripting algorithms and upscalings. Quickly learn how to compute fracture porosity, use mineral solvers, and more. Once you acquire the access to the learning data set, it will be available for the life-time term of your license. We regularly put new examples that you can install with a single click from your license.

NOTE: The extra data set is only intended for self-learning purposes. It must not be re-distributed. You purchase a server access to the extra data set for learning. The data set itself is not sold.


Following down is a selection of some relevant processed log examples, that ships out of the box with the Learn Set:

1. A clastic braided channels reservoir with a clay mineral solver.

2. A tight reservoir with a carbonate mineral solver and shale oil.

3. A carbonate and eolian reservoir with mineral solver and estimation of matrix density.

4. A clean eolian reservoir with indicator curve flags for net-sand and net-pay

5. An eolian reservoir with water saturation computed through the SW ratio method without porosity

6. A gas reservoir with porosity computed through five different methods: the Porosity in flushed zone equation from micro-resistivity, density porosity, neutron porosity, sonic porosity, and its benchmark against expanded core porosity on surface.



  1. A clastic braided channels reservoir with a clay mineral solver.

    GLOG File: claySolver.glog

    This example shows how estimate the major different clay minerals of a clastic reservoir. The last two log tracks display the clay composition through the semi-quantitative technique of X-Ray diffraction, and the solution found by the GeolOil clay mineral solver. Clay discrimination is always difficult. However the mineral solved estimations, show a reasonable qualitative match against the X-Ray diffraction reference track.

    The most dominant found clay is smectite. However, around the depth MD=1820 ft., the mineral solver correctly detected the presence of the clay mineral kaolinte, which is particularly difficult to detect as its Gamma Ray response is usually low due to its low Cation Exchange Capacity. Notice on the first Lithology track, how low is the GR Gamma Ray signal compared with signal yielded by a neutron porosity minus density porosity estimator VSH. See the log plot below ↓

    Mineral solved log plot for quartz, silt, and clay minerals: illite, smectite, kaolinite, and chlorite



    The functions panel work-flow below shows all the functions and equations used, including the volume of shale VSH that was computed combining the contrast between neutron porosity and density porosity estimator, with a GR based Larionov VSH estimator:

    GeolOil full example of functions work-flow to compute clay mineral solver





  2. A tight reservoir with a carbonate mineral solver and shale oil.

    GLOG File: shaleOil.glog

    This example shows a typical work-flow to process a tight reservoir with a mineral solver for clay and carbonate minerals. Also the deeper zone has a shale oil play for which TOC Total Organic Carbon is computed using the Schmoker equation calibrated with laboratory pyrolysis data. The track Minerals shows a reasonable agreement between mineral proportions estimated by lab XRD X-Ray Diffraction, and GeolOil mineral solver ↓

    Mineral solved log plot for clays, silt, quartz, calcite, and dolomite



    The sequential functions panel work-flow below ↓ shows the steps that produce the interpretation.

    GeolOil full example of functions work-flow to interpret a tight carbonate reservoir and a shale play




  3. A carbonate and eolian reservoir with mineral solver and estimation of matrix density.

    GLOG File: RHOM1_WebExample.glog

    An accurate estimation of the matrix density RHOM ρm is a fundamental task for any petrophysicist. In complex reservoirs ρm is no longer an approximate constant value inferred from the depositional environment, but significantly varies with depth. That is ρm = ρm(depth). Once RHOM is estimated, it is used to compute porosity and water saturation —and also to estimate permeability— The brown rightmost last track "RHO matrix" on the log plot below shows how to estimate the matrix density RHOM ρm using only well log curves, without any core lab measurements available ↓

    Log plot showing the estimation of matrix density RHOM using log curves information only



    The sequential functions panel work-flow below shows the steps that produce the interpretation.

    GeolOil full example of functions work-flow to estimate matrix density RHOM




  4. A clean eolian reservoir with indicator curve flags for net-sand and net-pay

    GLOG File: wellExampleNetPay.glog

    The example below ↓ shows a log plot with indicator curve flags (GeolOil exclusive trilean logic: -1 for no, +1 for yes, 0 for inconclusive) for net-sand and net-pay. After computing cutoffs for porosity and water saturation —this eolian rock is very clean, so computation of Vshale cutoff is waved: GR here does not read clay, but remnants of organic bio-radioactivity and signal noise—, the indicator flag curves are produced with the Filtering & Upscaling module.

    GeolOil log plot of Net-Sand and Net-Pay flags


    The Upscalings Panel work-flow below ↓ shows the steps that produce the indicator curves

    GeolOil Upscaling Panel to produce net-sand and net-pay indicator flag curves




  5. An eolian reservoir with water saturation computed through the SW ratio method without porosity

    GLOG File: Sw_Rxo_wellB.glog

    The example below ↓ shows in the fourth track, the water saturation computed by the technique of the SW ratio flushed zone method without using porosity (the red curve), and its comparison the water saturation computed by the Indonesia equation that uses porosity (the aqua-marine curve). The agreement between those curves is good, especially where the rock is less clayey.

    GeolOil software log plot of water saturation computed through SW ratio


    The sequential functions panel work-flow below ↓ shows a snippet of the steps that produce the interpretation.

    GeolOil software work-flow for water saturation computed through SW ratio




  6. A gas reservoir with porosity computed through five different techniques

    GLOG File: Phi_Rxo_and_Gas_DTF.glog

    The example below ↓ compares on the third track, five porosity computations from different methods: porosity from micro-resistivity, density porosity, neutron porosity, sonic porosity, and core porosity. Notice that the sampled core plugs seemed to be expanded at surface, increasing slightly the correct in-situ porosity.

    Well log plot with comparisons of porosity estimates from core, density porosity, neutron porosity, sonic porosity, and micro-resistivity porosity


    The sequential functions panel work-flow below ↓ shows a snippet of the steps that produce the interpretation.

    GeolOil software work-flow to estimate porosity from flushed zone micro-resistivity




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