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The term ''difficult lithologies'', as addressed in this chapter, refers to a formation composed of two or more mineralogies. The presence of two or more minerals significantly increases the difficulty of determining both [[porosity]] and lithology from wireline logs. This chapter reviews some common techniques that can be used to solve for lithology and porosity. It also addresses some commonly encountered lithologies and their characteristics relative to log responses.

The term ''difficult lithologies'', as addressed here, refers to a formation composed of two or more mineralogies. The presence of two or more minerals significantly increases the difficulty of determining both [[porosity]] and lithology from wireline logs. This article reviews some common techniques that can be used to solve for lithology and porosity. It also addresses some commonly encountered lithologies and their characteristics relative to log responses.

==Identifying the occurrence of difficult lithologies==

==Identifying the occurrence of difficult lithologies==

* Analysis of log responses

* Analysis of log responses

The methods to determine the occurrence of difficult lithologies from the first six sources just listed are not covered in this part of the volume (Part 4). (For information on these sources, see the chapters on “[[Mudlogging: Drill cuttings analysis]]” and “[[Mudlogging: The mudlog]]” in Part 3; also see “[[Core description]]” in Part 5.)

The methods to determine the occurrence of difficult lithologies from the first six sources just listed are not covered in this article. (For information on these sources, see “[[Mudlogging: Drill cuttings analysis]]” and “[[Mudlogging: The mudlog]]”; also see “[[Core description]]”.)

Identifying the occurrence of difficult lithologies from logs can be formidable. Two crossplot techniques are commonly used to identify the occurrence of mineralogies: (1) the M-N crossplot and (2) the MID crossplot.

Identifying the occurrence of difficult lithologies from logs can be formidable. Two crossplot techniques are commonly used to identify the occurrence of mineralogies: (1) the M-N crossplot and (2) the MID crossplot.

UMAA can be determined from the chart in Figure 2. Start by marking the Pe value on the photoelectric portion of the horizontal axis (left side), then go vertically to the bulk density value. Next, move horizontally to the apparent total porosity, and then down to the UMAA value.

UMAA can be determined from the chart in Figure 2. Start by marking the Pe value on the photoelectric portion of the horizontal axis (left side), then go vertically to the bulk density value. Next, move horizontally to the apparent total porosity, and then down to the UMAA value.

[[file:difficult-lithologies_fig2.png|thumb|{{figure number|2}}UMAA determination chart. Copyright: Schlumberger Educational Services, 1989.]]

[[file:difficult-lithologies_fig2.png|thumb|{{figure number|2}}UMAA determination chart. © Schlumberger Educational Services, 1989.]]

Now you can cross plot the RHOMAA and UMAA values on the chart in Figure 3. Binary mixtures of minerals plot along a line connecting the two mineral points. Ternary mixtures of minerals plot in a triangle connecting the three mineral points. Arrows indicate the affects of gas, secondary porosity, salt, barite, and heavy minerals.

Now you can cross plot the RHOMAA and UMAA values on the chart in Figure 3. Binary mixtures of minerals plot along a line connecting the two mineral points. Ternary mixtures of minerals plot in a triangle connecting the three mineral points. Arrows indicate the affects of gas, secondary porosity, salt, barite, and heavy minerals.

[[file:difficult-lithologies_fig3.png|thumb|{{figure number|3}}MID plot for mineral identification. Copyright: Schlumberger Educational Services, 1989.]]

[[file:difficult-lithologies_fig3.png|thumb|{{figure number|3}}MID plot for mineral identification. © Schlumberger Educational Services, 1989.]]

==Techniques for analyzing difficult lithologies==

==Techniques for analyzing difficult lithologies==

Each logging measurement can be expressed in a response equation that relates the recorded log to the volumetric components of lithology and fluids. (For these response equations expressed in their most basic configuration, see the chapter on “Standard Interpretation” in Part 4.) These basic equations can be expanded to include any number of mineralogies and fluids.

Each logging measurement can be expressed in a response equation that relates the recorded log to the volumetric components of lithology and fluids. (For these response equations expressed in their most basic configuration, see “[[Standard interpretation]]”.) These basic equations can be expanded to include any number of mineralogies and fluids.

All the logging response equations can then be set up for each measurement, such as the density, neutron, and sonic. The unknowns must be less than or equal to the number of equations for a unique solution to be obtained. A paramount fact that must be kept in mind is that the number of variables that are being computed cannot exceed the number of equations. For example, if the density, neutron, and sonic logs are being used, the total number of equations that can be set up is four—one for each of the measured logs and the fourth for the material balance equation (exemplifying that the sum of all constituents equals 100% of the volume of the rock). Thus, in this case, only four variables can be computed. Assumptions and local knowledge can be used to constrain the problem by reducing the amount of unknown knowledge.

All the logging response equations can then be set up for each measurement, such as the density, neutron, and sonic. The unknowns must be less than or equal to the number of equations for a unique solution to be obtained. A paramount fact that must be kept in mind is that the number of variables that are being computed cannot exceed the number of equations. For example, if the density, neutron, and sonic logs are being used, the total number of equations that can be set up is four—one for each of the measured logs and the fourth for the material balance equation (exemplifying that the sum of all constituents equals 100% of the volume of the rock). Thus, in this case, only four variables can be computed. Assumptions and local knowledge can be used to constrain the problem by reducing the amount of unknown knowledge.

===Shaly sandstones===

===Shaly sandstones===

The interpretation method best suited for shaly sandstones is dependent upon the distribution of shale, the clay type, the mineralogy of the silt fraction, and the resistivity of water within the sandstones. The classic approach is the sand-silt-shale method introduced by Poupon et. al<ref name=pt04r10>Poupon, A., Hoyle, W. R., Schmidt, A. W., 1971, Log analysis in formations with complex lithologies: Journal of Petroleum Technology.</ref>. An approximate correction for a single heavy mineral was provided for in this approach. Silt is considered to be primarily quartz. Volume of clay, volume of silt, and porosity are determined from interpolation of the density-neutron crossplot. Matrix response points are defined for sand and silt, water, and dry clay minerals. A wet clay point is defined on the dry clay minerals-100% water line. A shale point was defined on the quartz-wet clay line. The model can then determine porosity, shale volume, and silt index from interpolation in this framework. Water saturation can be determined using an appropriate shaly sandstone resistivity equation. This method does not adequately address the more complex case of shaly sandstones with variable volumes of feldspar, mica, or carbonate material. This model can be solved using the graphical, linear matrix, or least squares minimization method.

The interpretation method best suited for shaly sandstones is dependent upon the distribution of shale, the clay type, the mineralogy of the silt fraction, and the resistivity of water within the sandstones. The classic approach is the sand-silt-shale method introduced by Poupon et. al.<ref name=pt04r10>Poupon, A., Hoyle, W. R., Schmidt, A. W., 1971, Log analysis in formations with complex lithologies: Journal of Petroleum Technology.</ref> An approximate correction for a single heavy mineral was provided for in this approach. Silt is considered to be primarily quartz. Volume of clay, volume of silt, and porosity are determined from interpolation of the density-neutron crossplot. Matrix response points are defined for sand and silt, water, and dry clay minerals. A wet clay point is defined on the dry clay minerals-100% water line. A shale point was defined on the quartz-wet clay line. The model can then determine porosity, shale volume, and silt index from interpolation in this framework. Water saturation can be determined using an appropriate shaly sandstone resistivity equation. This method does not adequately address the more complex case of shaly sandstones with variable volumes of feldspar, mica, or carbonate material. This model can be solved using the graphical, linear matrix, or least squares minimization method.

The solution for the complex case of sandstones with feldspar, mica, and carbonate material was resolved after log analysts became comfortable with the new spectral gamma ray (K, Th, and U) and photoelectric (Pe) measurements. The spectral gamma ray log is helpful in sandstones containing potassium feldspars or thorium-bearing clays. The natural gamma ray spectra, Pe, density, and neutron expanded response equations can be combined to solve for porosity and to estimate volumes of calcite, quartz, dolomite, clay, feldspar, anhydrite, and salt. Once porosity is determined, saturation can be estimated from the appropriate shaly sandstone resistivity equation. This model is too complex to address using graphical methods and must be done using the linear matrix or least squares minimization method.

The solution for the complex case of sandstones with feldspar, mica, and carbonate material was resolved after log analysts became comfortable with the new spectral gamma ray (K, Th, and U) and photoelectric (Pe) measurements. The spectral gamma ray log is helpful in sandstones containing potassium feldspars or thorium-bearing clays. The natural gamma ray spectra, Pe, density, and neutron expanded response equations can be combined to solve for porosity and to estimate volumes of calcite, quartz, dolomite, clay, feldspar, anhydrite, and salt. Once porosity is determined, saturation can be estimated from the appropriate shaly sandstone resistivity equation. This model is too complex to address using graphical methods and must be done using the linear matrix or least squares minimization method.

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For a more complete explanation of water saturation equations and their terms, refer to Worthington<ref name=pt04r22>Worthington, P., 1985, The evolution of shaly-sand concepts in reservoir evaluation: The Log Analyst.</ref> or Patchett and Herrick<ref name=pt04r9>Patchett, J. G., Herrick, D. C., 1982, A review of saturation models: SPWLA Reprint Volume Shaly Sands, SPWLA.</ref>. The Simandoux and Indonesia equations were designed mainly for relatively salty formation waters and moderate amounts of dispersed clay. The dual water and Waxman and Smits equations were designed for all water salinities and moderate amounts of dispersed clays.

For a more complete explanation of water saturation equations and their terms, refer to Worthington<ref name=pt04r22>Worthington, P., 1985, The evolution of shaly-sand concepts in reservoir evaluation: The Log Analyst.</ref> or Patchett and Herrick.<ref name=pt04r9>Patchett, J. G., Herrick, D. C., 1982, A review of saturation models: SPWLA Reprint Volume Shaly Sands, SPWLA.</ref> The Simandoux and Indonesia equations were designed mainly for relatively salty formation waters and moderate amounts of dispersed clay. The dual water and Waxman and Smits equations were designed for all water salinities and moderate amounts of dispersed clays.

Recommended logs to use for interpreting shaly sandstones are

Recommended logs to use for interpreting shaly sandstones are