Petrophysical applications commercially available (like Geolog®, Techlog®, PowerLog®, LESA®, etc.) have a multimineral analysis module that requires the mineral and fluids coefficients (also called end-points) to be well known. When performing mutlimineral analysis the petrophysicist needs to input the values of each coefficient or use the default values provided by the software. After modeling synthetic logs, the petrophysicist then modifies the coefficients until he/she is satisfied with the agreement between observed and modeled logs. For simple lithologies or mineral compositions this process is fairly straight forward. As we develop reservoirs with more complex lithologies, however, our uncertainty about the values of the coefficients increases. In these instances, the petrophysicist needs to adjust the coefficients in a trial-and-error fashion until a satisfactory result is achieved. Since the coefficients may be dependent on one another, a change in one coefficient typically requires changes in other coefficients. iMineralysis™ automates this trial-and-error process and produces resuts where the coefficients are consistent among themselves. iMineralysis™ allows you to set the initial set of coefficients and a range of a suspected uncertainty. Then, iMineralysis not only then performs the estimation of mineral fractions but also intelligently tests hundreds of combinations within the specified ranges by using a genetic algorithm based optimization. Basically, it performs the multimineral analysis for hundreds of possible cases in seconds! With iMineralysis™ the petrophysicist can achieve a solution that can be impossible or too time consuming with any other commercial tool as it can require too many tests. Besides automating the trial-and-error process in the multimineral analysis, iMineralysis™ provides diagnostics that can help determine the non-uniqueness of the solutions. Using these diagnostic tools, the user can identify which mineral constants need additional constraints in order to reduce the non-uniqueness. This capability is not available in other commercially available applications of multimineral analysis.

The values of the mineral constants that are typically used as input for multimineral analysis are related to pure minerals (quartz, calcite, dolomite, etc.). In reality, however, the rocks are formed by a complex mix of minerals, even in areas that we consider to be mineralogically simple. For instance, in intervals where only sand and clay are present, the petrophysicist tends to select quartz and illite as the basic minerals that make the rock even though in clastic environments we know that the composition can be more complex. Quartz is often accompanied by feldspars, and clay usually comes in a mix of illite, smectite, and mica. Those individual minerals are very difficult, if not impossible, to solve by using logs only and therefore, the petrophysicist usually solves for the mixes that results in sand and clay. Since the properties available in the literature are for the pure minerals but it is not possible to for solve for them individually, the petrophysicist needs to vary the initial constants associated to each mix until he/she achieves an optimal solution. iMineralysis™ helps the petrophysicist to obtain this solution by intelligently testing hundreds of possibilities.

By estimating the matrix of coefficients using wells from different areas and different intervals, you can estimate spatial variations in water resistivity, kerogen maturity, and clay composition. You can also: Identify by-passed-pay Identify kerogen grain-density trends vs. thermal maturity Independently quantify variable Rw trends Create effective hydrocarbon pore volume ranges Perform uncertainty analysis for petrophysics constraints in reservoir models Create lithology-based geomechanics-constraints

The constituents are the minerals and fluids of the system you are trying to solve for. When you select the number of constituents it includes both minerals and fluids. The typical system to use has a minimum of 2 minerals and 2 fluids, but depending on the application you can also have only one fluid (water, for instance), or more minerals depending on the number of logs available and the complexity of the mineral composition.

Of course! iMineralysis provides tools to guide the less experienced user that can be deactivated by the expert or when he/she becomes more confident in the use of the software.

No. The estimation of the mineral coefficients is a highy non-unique and nonlinear problem that requires as much information and constraints as you are able to provide. These constraints may come in the form of expected mineralogy (from core descriptions), initial values from the literature, expected ranges of variations for each coefficient and internal relations between coefficients. Once you provide the initial information, iMineralysis™ can help you to refine it while honoring your input well logs. iMineralysis™ also provides a few pre-build models for different geological scenarios that can be modified by the user to meet his/her specific needs.

The simultaneous inversion of all fractions (volumes) of components is based on the assumption that the measured logs are a linear combination of such fractions and the tool responses for each component. However, the resistivity log is not linearly related to the volume of the components of the system. In this case, the resistivity tool can be linearized by using a "pseudo-conductivity" instead of the resistivity. The Cx corresponds to one over resistivity elevated to the root of the "m" exponent (1/Rt)^(1/m) of the Archie equation. In iMineralysis™, the water saturation model assumed is a modified Archie equation with m=n.

Take the resistivity of the water (or clay) and calculate the constant Cx as the inverse of the resistivity elevated to the "m" power (where "m" is the cementation exponent from Archie's equation). See "What water saturation model does iMineralysis use?".

The water saturation model assumed is a modified Archie equation with m=n, which means that the resistivity curve MUST be transformed to pseudo-conductivity (Cx) as explained above.

No, it must be transformed into pseudo-resistivity Cx first. See above.

Wet. iMineralysis™ considers the clay and its bound water as a whole mineral.

U is the volumetric photoelectric factor. The litho-density log tool measures the Photoelectric Factor (PE). The simultaneous inversion of all logs is based in the assumption that the log responses of each mineral/fluid are linearly related with the volume of each component. Although the PE tool doesn’t relate linearly with the volume of the components of the system, the photoelectric factor U estimated as PE*RHOB does.

Yes. Any additional log curves you may have can help to improve the results. Examples are total porosity from crossplot calculations or from NMR tools or Kerogen from different methodologies.

Any calculated curves that you want to use inside iMineralysis™ optimization MUST be generated outside iMineralysis™. For now, iMineralysis™ doesn’t provide the option to perform basic operations with logs. Examples of pre-calculations that you may want to peform before using iMineralysis™ are: pseudo-conductivity to calculate saturations (see FAQ on what is Cx), volumetric photo-electric factor (U), and kerogen volume from Passey method among others.

Log data is expected to be in LAS format. Please what this video for more details on how to load an LAS file:

You can load as many wells as you'd like and perform mutimineral analysis in all of them or a selected subset at the same time. You will also have to select which curves in each well you want to use for the optimization.

No, you can load more logs than what you may need for the multimineral optimization (recommended). Once loaded, you will need to specify which logs you want to use, or setup different optimizations for different list of logs. It is also recommended you load logs that can help you select the valid samples to be used in the optimization (for example use a caliper log to make sure you exclude samples affected by washouts).

Please watch this video for details:

Formation tops can be loaded as part of the LAS data for each well in standard Petra® format: ~tops #TOP NAME TOP MD #-------------------------- ---------------- T1 2316 T2 2350 T3 2420 T4 2443 Alternatively, formation tops can be also read from a .csv file that contains the following information: WELLNAME, TOP name, MD (measured depth)

You can export LAS files with the modeled curves and fractions of constituents that result from the optimization. Additionally, you can export a CSV file with optimized matrix coefficients, average fractions, and errors for each well and each optimization. This output can be used for mapping results in other applications. Alternatively, you can copy the optimized matrix of coefficients for mineral and fluids from the screen into a spreadsheet. You can also save the matrices of search limits and coefficients in internal iMineralysis™ format for later use in other iMineralysis™ projects

In iMineralysis™, the model for the optimization consists of logs, constituents (minerals and fluids), matrix of ranges (search limits) for constituents coefficients and matrix of single coefficients of constituents (which are the ones used for other commencial multimineral analysis applications). At the heart of iMineralysis™ is the definition of the search limits matrix. For more details, watch this tutorial video:

A genetic algorithm is a heuristic (self-educating) search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology. Genetic algorithms are excellent for searching through large and complex data sets. They have proven very effective in the solution of nonlinear problems that are combinatorial in nature, like the problem of petrophysical multimineral analysis.

Genetic algorithms are a part of AI.

Absolutely! After many years of use in commencial application, we've selected a set of parameters for the generic algorithm that work very well for a wide range of data types and geologic conditions. However, you'll still be able to change some parameters to better adapt the optimization to your particular problem. Look for "Options" in the "Setup Optimization" menu for more details.

Yes! And it is recommended. By bringing more wells into the optimization, you can obtain (in one run) a matrix of coefficients that is optimal for all selected wells. Alternatively, you can also run separate optimizations for each well if you expect that the matrix of coefficients will change from one well to the other.

You can create different models for different intervals defined with formation tops. This results in a more robust optimization by estimating only the coefficients of the components that are actually present in each interval. For more details watch the tutorial video:

Probably not. Many different models for minerals and fluids may fit the data equally well, hence the importance of constraining the coefficients as much as possible. Factors that contribute to increase the non-uniqueness are: 1) Log data inconsistencies/problems due to bad borehole conditions and noise 2) Highly correlated input logs in the interval of interest 3) Wide search range for matrix coefficients. iMineralysis™ provides different measures on non-uniqueness for the global solutions and for individual constituents for the user to judge how severe the non-uniqueness may be. To reduce non-uniqueness, the used should check for the log data quality in the interval of interest, verify that the no input log is highly correlated to another and reduce the search range for the different constituents as much as possible. Another ways to reduce non-uniqueness are by increasing the number of independent input logs, by reducing the number of constituents to solve for or by using tighter contraints in coefficients where the non-uniqueness is more severe.

Make sure then when you select the logs for the optimization, they are used as equality instead of inequality constraints.

All plots have three background colors: Green (good), Yellow (intermediate), and Red (poor). Three curves are displayed: Blue (optimal individual for that generation), Green (average individual for that generation) and Red (worst individual for that iteration). Ideally, we want Blue curves to be in the Green background zones. This is the meaning of the different plots: Coefficient Matrix Fitness: The nonlinear optimization aims to maximize the fitness between measured and modeled logs. This plot represents how this fitness changes as iteration increase. Tolerance Error: Average error in tolerance units between the estimated and observed curves. % Error: Average relative error between the estimated and observed curves. % Total Volume Error: Average amount by which the sum of the modeled fractions differs from 1. Problem flags: Average penalty imposed for fraction, liquid, coefficient, and condition number violations. % Fraction violations: Percentage of the analyzed samples where any of the modeled fractions is less than -0.01 or more than 1.01. % Severe Fraction violations: Percentage of the analyzed samples where any of the modeled fractions is less than -0.1 or more than 1.1. % Liquid violations: Percentage of the analyzed samples where the sum of the modeled fluid fractions is more than 0.45. % Severe Liquid Violations: Percentage of the analyzed samples where the sum of the modeled fluid fractions is more than 0.55. % Coefficient Violations: Percentage of model coefficients that are within 5% of either endpoint of the allowed range in the limits matrix. This is taken to indicate that the model is having trouble fitting he observed logs with “reasonable” constituent properties. % Severe Coefficient Violations: Percentage of model coefficients that are within 5% of either endpoint of the allowed range in the limits matrix. Matrix condition number: Log10 of the ratio of the largest and smallest nonzero singular values of the model coefficient matrix. Large condition number means that the linear system is unstable, and the modeled curves and fractions are unreliable.

After you set up the matrix of coefficients, you can either perform the optimization to refine the initial matrix coefficients or perform a single forward modeling (generate synthetic logs and constituents volumes) based on those coefficients. This is equivalent to running only one iteration of the optimization with population size equal to one. Look for "Prepare Optimization" in the "Optimize" menu.

It is the maximum difference that you will allow between modeled and measured logs. The tolerance is used in the error calculation during the optimization process. Therefore, we have to make sure to use a reasonable tolerance for each log. For instance, a reasonable tolerance in the prediction of the density log may be +/- 0.01 or +/-0.02 but it's not reasonable to use +/-1. The tolerance is related to the precision of the log but also to the confidence the user has in the log (quality of the data). iMineralysis™ uses default tolerances that can be changed by the user.

No. At this point iMineralysis™ does not allow any operations between curves.

The data set used in the tutorial comes with the installation package.

Please watch this video for details: https://www.youtube.com/watch?v=Hg1AmBBo9T4

Please watch this video for details:

Please watch this video for details:

Please watch this video for details:

Please watch this video for details:

Please watch this video for details:

You can copy the optimized matrix of coefficients for mineral and fluids for the display screen into a spreadsheet and save the curves with the fractions of constituents that results from the optimization in LAS format. You can also save the matrices of search limits and coefficients in internal iMineralysis format for later use in other projects.

The Demo license expires in 14 days whereas the Premium license expires in 1 year. Also, the Demo license has a watermark in the displays that the Premium license does not have. Otherwhise, the two versions have identical capabilities so you can perform a full evaluation of the software for your specific needs.

30 days.

If the demo license expires before you have completed your testing, you can download it again in order to complete what you are doing. However, we may require that you upgrade to the Premium version after several downloads.

Go to the Subscription page, look for the "Cancel" button and follow the instructions.

Only one user can access the license. The license is single system, single user.

No. You have to pruchase the Premium version to make displays without the watermark.

You can pay in your local currency from many countries. However, for different regulations, you may still need to pay in US dollars in countries where US dollars is not the local currency. Check at the time of completing your subscription.

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Limited email support. We will try to address general questions in this section of FAQs.

Windows 64-bit, version 7 or higher Screen Resolution 1280 x 800 or higher Recommended: Java Runtime Environment version 8 or higher

Suggestions are always welcome! You can send them to support@seispetro.com

Please send us a note explaining the problem to support@seispetro.com. We will fix it and make a new version available for download as soon as we can.

Take a look at these resources: 1) Quick reference manual that can be accessed from the System window of the software. 2) Online Mini-help (that can be accessed by clicking the "?" in the upper left of each view window of the software 3) Tutorial videos on the Seispetro Geosoftware channel. 4) These FAQs.

We've made every effort to reduce antivirus "false positives" but we cannot guarantee no false positives for any of the many antivirus applications available in the market. We recommend that you report the application to your antivirus company by sending the .exe file you downloaded. You can also search for the "Exception" settings of the antivirus where you can specify that it's ok to use iMineralysis™.