iMineralysis frequently asked questions

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:

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.

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:

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.

Petrophysical software 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 petrophysicists need 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 posible 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 than 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.

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.

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.

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, petrophycists tend to select quartz and illite as the basic minerals that make the rock although in clastic enviroments we know that the composition can be more complex. Quartz is often acompanied 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 is not possible to for solve for them individually, the petrophysicst needs to vary the initial constants associated to each mix until he/she achieves an optimal solution. iMineralysis™ helps the petrophycist to obtain this solution by intelligently testing hundreds of posibilities.

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.

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.

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).

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).

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.

No. The estimation of the mineral coefficients is a highy non-unique and non-linear 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 used to meet his/her specific needs.

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.

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.

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.

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.

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)

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

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.

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.

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.

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 is 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.

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™.

Frequently asked questions

Frequently Asked Questions​

How do I setup a model for optimization?

What do I need to do before I try iMineralysis™ with my own data?

Where can I find the data set used in the tutorial?

What is the difference between a Premiun license and a Demo license?

What is the difference between a Premiun license and a Demo license?

How do I setup a model for optimization?

What do I need to do before I try iMineralysis™ with my own data?

Where can I find the data set used in the tutorial?

What is the difference between iMineralysis™ and other commercial applications for multimineral analysis?

What is the difference between iMineralysis™ and other commercial applications for multimineral analysis?

How do I load the data?

What is a genetic algorithm? Why iMineralysis™ uses a genetic algorithm?

Why is so difficult to determine the mineral constants even in areas of moderate lithological complexity?

What is a genetic algorithm? Why iMineralysis™ uses a genetic algorithm?

For how long is the Demo license active?

Why is it so difficult to determine the mineral constants even in areas of moderate lithological complexity?

How do I load the data?

For how long is the Demo license active?

What log data formats can I load?

What log data formats can I load?

Any other interesting applications of iMineralysis™?

How many Demo licenses can I download?

Any other interesting applications of iMineralysis™?

What is the relation between genetic algorithms and Artificial Intelligence (AI)?

I don't know anything about genetic algorithms. Will I be able to use the software?

How do I display log curves?

How do I display log curves?

How many Demo licenses can I download?

What kind of support do you offer?

Can I use multiple wells to perform the optimization?

How many wells can I load at the same time?

What are the "constituents " (or "components")?"

How do I select the curves for analysis?

How do I select the curves for analysis?

What kind of support do you offer?

What are the "constituents " (or "components")?"

I don't know anything about genetic algorithms. Will I be able to use the software?

How many wells can I load at the same time?

How do I cancel my subscription?

I’m a petrophysicist with limited experience. Will I be able to use the software?

How many users can access a license at a time?

What are the system requirements to run iMineralysis™?

How do I select the samples that will be used in the optimization?

I’m a petrophysicist with limited experience. Will I be able to use the software?

Do I have to load only the logs that will be used in the optimization?

How many users can access a license at a time?

Can I use multiple wells to perform the optimization?

Can I remove the watermark from the displays in the Demo license?

Do I have to load only the logs that will be used in the optimization?

Can I remove the watermark from the displays in the Demo license?

My mineral composition changes in different geologic interval. How do I create different models for different intervals?

How do I select the samples that will be used in the optimization?

I don't know anything about the mineral coefficients in my area. Can I use iMineralysis™ to estimate them with no additional information?

I don't know anything about the mineral coefficients in my area. Can I use iMineralysis™ to estimate them with no additional information?

Can I pay using my local currency?

Where can I send suggestions regarding software improvements?

Can I pay using my local currency?

How do I select which log samples to use for the optimization?

Are the solutions unique?

My mineral composition changes in different geologic interval. How do I create different models for different intervals?

How do I define the intervals of interest for the optimization?

Where can I send suggestions regarding software improvements?

How do I select which log samples to use for the optimization?

How do I define the intervals of interest for the optimization?

How do I set up a coefficient model?

What is the curve "Cx" in the test data?"

I selected the same number of logs and constituents but I get a message saying that the system is underdetermined. What's going on?

Do I have to pay sales taxes/VAT?

Are the solutions unique?

If I find a bug in the software, what do I do?"

If I find a bug in the software, what do I do?"

Do I have to pay sales taxes/VAT?

What is the curve "Cx" in the test data?"

How do I set up a coefficient model?

How do I load formation tops (well markers)?

How do I set up and run the actual optimization?

What’s the relationship between SeisPetro Geosoftware, LLC and FastSpring?"

How do I load formation tops (well markers)?

Frequently Asked Questions