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Log analysis is undergoing major changes through the addition of new logging tools and improved computerized interpretation software. These features make accurate and detailed geological descriptions from wireline data feasible. Now, wireline data are being integrated with geological, geophysical, and engineering data through software to produce more accurate and comprehensive answers to geological questions. This chapter briefly summarizes computerized log analysis packages (LAPs) by reviewing basic features and emphasizing the fundamental ideas that make LAPs useful, flexible, and powerful tools for development geologists.

Log analysis is undergoing major changes through the addition of new logging tools and improved computerized interpretation software. These features make accurate and detailed geological descriptions from wireline data feasible. Now, wireline data are being integrated with geological, geophysical, and engineering data through software to produce more accurate and comprehensive answers to geological questions. This article briefly summarizes computerized log analysis packages (LAPs) by reviewing basic features and emphasizing the fundamental ideas that make LAPs useful, flexible, and powerful tools for development geologists.

==Features==

==Features==

Log analysis packages usually store data in a depth-oriented database. This directly associates a well's scientific data (including wireline traces, core data, tops, and test intervals) with the specific depths of their occurrence. Depending on the LAP used, database depth values may change (1) by a constant increment (usually based on the smallest common sampling increment) or (2) by varying increments (based on each trace's unique sampling nature).

Log analysis packages usually store data in a depth-oriented database. This directly associates a well's scientific data (including wireline traces, [[Overview of routine core analysis|core data]], tops, and test intervals) with the specific depths of their occurrence. Depending on the LAP used, database depth values may change (1) by a constant increment (usually based on the smallest common sampling increment) or (2) by varying increments (based on each trace's unique sampling nature).

To use this data successfully for display and calculations, the user needs to learn the purpose and method of operation for each of the seven basic LAP features. They are

To use this data successfully for display and calculations, the user needs to learn the purpose and method of operation for each of the seven basic LAP features. They are

==Data input==

==Data input==

''Digital data'' are created by writing values into a file in numerical form. Wireline data are now routinely captured on a magnetic medium at predetermined sample increments. Increments vary from tool to tool, from service company to service company (even for comparable tools), and according to the depth recording system (English or metric) used by the service company. The quantity of data values recorded at a given depth increment can also vary. Most logging tools record only one value per increment for each specific trace (slow channel data). Others (for example, a full waveform acoustic tool) acquire multiple values at each depth increment (fast channel data) in order to later replicate and use the entire acquired range of values.

''Digital data'' are created by writing values into a file in numerical form. Wireline data are now routinely captured on a magnetic medium at predetermined sample increments. Increments vary from tool to tool, from service company to service company (even for comparable tools and [http://essaywriting.center essay writing service]), and according to the depth recording system (English or metric) used by the service company. The quantity of data values recorded at a given depth increment can also vary. Most logging tools record only one value per increment for each specific trace (slow channel data). Others (for example, a full waveform acoustic tool) acquire multiple values at each depth increment (fast channel data) in order to later replicate and use the entire acquired range of values.

Each wireline company has its own proprietary format for recording digital data. The two most common formats are LIS (the de facto standard) and BIT. Considerable efforts are being made to standardize all of the various formats into a single industry-wide standard known as the API digital log interchange standard, or DLIS.<ref name=pt08r8>Froman, N. L., 1989, DLIS—API Digital Log Interchange Standard: The Log Analyst, v. 30, n. 5, p. 390–394.</ref>

Each wireline company has its own proprietary format for recording digital data. The two most common formats are LIS (the de facto standard) and BIT. Considerable efforts are being made to standardize all of the various formats into a single industry-wide standard known as the API digital log interchange standard, or DLIS.<ref name=pt08r8>Froman, N. L., 1989, DLIS—API Digital Log Interchange Standard: The Log Analyst, v. 30, n. 5, p. 390–394.</ref>

==Data editing==

==Data editing==

Options for editing of the data include the following:

Options for editing of the data include the following:

* Discriminate against invalid data. Using this technique, the user specifies minimum and/or maximum value limits for a primary trace. Then, instead of modifying actual trace values, the trace is scanned, and at depths where data occur outside the limits, flags are set in a separate discriminator trace. When applying the discriminator trace during data displays or calculations, any depths containing flags will either be eliminated from the display or be assigned default calculation values.

* Discriminate against invalid data. Using this technique, the user specifies minimum and/or maximum value limits for a primary trace. Then, instead of modifying actual trace values, the trace is scanned, and at depths where data occur outside the limits, flags are set in a separate discriminator trace. When applying the discriminator trace during data displays or calculations, any depths containing flags will either be eliminated from the display or be assigned default calculation values.

* Apply depth corrections. These fall into two categories:

* Apply depth corrections. These fall into two categories:

*#Depth shifting traces against each other. To do this, the user visually compares base and unshifted traces, marks corresponding data points (Figure 1), and then shifts the off-depth data to the base trace depths.

*#Depth shifting traces against each other. To do this, the user visually compares base and unshifted traces, marks corresponding data points ([[:file:log-analysis-applications_fig1.png|Figure 1]]), and then shifts the off-depth data to the base trace depths.

*#Correct for true vertical depth (TVD), true vertical thickness (TVT), and/or true stratigraphic thickness (see [[Preprocessing of logging data]]).

*#Correct for true vertical depth (TVD), true vertical thickness (TVT), and/or true stratigraphic thickness (see [[Preprocessing of logging data]]).

* Baseline the spontaneous potential (SP). Interactively flattening the SP to a shale baseline at a single value (Figure 2) allows the user to look at SP values quantitatively in order to calculate water resistivity (''R''_w) and estimate shale content.

* Baseline the [[Basic open hole tools#Spontaneous potential|spontaneous potential (SP)]]. Interactively flattening the SP to a shale baseline at a single value ([[:file:log-analysis-applications_fig2.png|Figure 2]]) allows the user to look at SP values quantitatively in order to calculate water resistivity (''R''_w) and estimate shale content.

* Convert data scales (both ways): conductivity to resistivity, raw data to porosities, neutron porosities to a different matrix, metric to English depth units, percent to decimal, and so on.

* Convert data scales (both ways): conductivity to resistivity, raw data to porosities, neutron porosities to a different matrix, metric to English depth units, percent to decimal, and so on.

* Data normalization. This procedure assumes the values in an individual data trace are credible but require some modification. This involves modifying data values with an atypical distribution and/or range to a “normal” distribution and range (see Figure 3 and discussion of histograms). Proper normalization must first account for borehole conditions during each run, ''and'' geological changes taking place across a wider geographic area. Normalization is accomplished by applying the equation:

* Data normalization. This procedure assumes the values in an individual data trace are credible but require some modification. This involves modifying data values with an atypical distribution and/or range to a “normal” distribution and range (see [[:file:log-analysis-applications_fig3.png|Figure 3]] and discussion of histograms). Proper normalization must first account for borehole conditions during each run, ''and'' geological changes taking place across a wider geographic area. Normalization is accomplished by applying the equation:

:<math>y = ax + b</math>

:<math>y = ax + b</math>

where

where

** ''a'' = constant (distribution) multiplier value

** ''a'' = constant (distribution) multiplier value

**''x'' = trace to be normalized

**''x'' = trace to be normalized

**''b'' = constant (range) value (Figure 3)

**''b'' = constant (range) value ([[:log-analysis-applications_fig3.png|Figure 3]])

* Rename, copy, and delete curves.

* Rename, copy, and delete curves.

==Data display==

==Data display==

Data display is the most frequently used LAP feature. Properly displayed data allows users to do the following:

Data display is the most frequently used LAP feature. Properly displayed data allows users to do the following:

===Traceplots===

===Traceplots===

''Traceplots'' (''TPLTs'') visually relate data values to ''depth''. When planning any TPLT display, careful use of display variables (including scales, intervals, grids, track quantifies and widths, number of curves, line types and weights, colors, symbols, spacing, shading, and annotation) can be used to convey an immense amount of information without overwhelming an observer (Figure 4). Some of the more powerful LAPs allow interactive TPLT display, correlation, and database storage of formation tops from one or more wells displayed simultaneously on the screen.

''Traceplots'' (''TPLTs'') visually relate data values to ''depth''. When planning any TPLT display, careful use of display variables (including scales, intervals, grids, track quantifies and widths, number of curves, line types and weights, colors, symbols, spacing, shading, and annotation) can be used to convey an immense amount of information without overwhelming an observer ([[:file:log-analysis-applications_fig4.png|Figure 4]]). Some of the more powerful LAPs allow interactive TPLT display, correlation, and database storage of formation tops from one or more wells displayed simultaneously on the screen.

 

===Crossplots===

===Crossplots===

''Crossplots'' (''XPLTs'') relate two or more different trace values to each other at the same depth, such as core [[porosity]] and bulk density. Each type of wireline tool measures a different rock property. By studying the same XPLT in many wells, distinctive data plot patterns related to these rock properties allow users to identify lithologies, porosities, parameters, and other geological and/or engineering relationships.

''Crossplots'' (''XPLTs'') relate two or more different trace values to each other at the same depth, such as core [[porosity]] and bulk density. Each type of wireline tool measures a different rock property. By studying the same XPLT in many wells, distinctive data plot patterns related to these rock properties allow users to identify lithologies, porosities, parameters, and other geological and/or engineering relationships.

Several interactive graphic XPLT techniques and other features have been developed that make crossplots even more useful and powerful:

Several interactive graphic XPLT techniques and other features have been developed that make crossplots even more useful and powerful:

* Pickett plots, which are log-log plots of resistivity versus porosity (Figure 5) allow interactive parameter identification of m (the cementation exponent) and the product (''a'' × ''R''_''w'') (empirical constant × formation water resistivity), as well as visually displaying water saturation (S_w).

* Pickett plots, which are log-log plots of resistivity versus porosity ([[:file:log-analysis-applications_fig5.png|Figure 5]]) allow interactive parameter identification of m (the cementation exponent) and the product (''a'' × ''R''_''w'') (empirical constant × formation water resistivity), as well as visually displaying water saturation (S_w).

* Polygon isolators drawn on the screen around patterns recognized by the user (left side of Figure 6) identify the enclosed data in the database for future reference. A dual XPLT/TPLT screen display of the same data (Figure 6) allows XPLT pattern recognition and isolation, and TPLT depth identification (usually with tic marks or color shading at the corresponding TPLT depths). The reverse procedure (TPLT depth interval isolation and XPLT identification) is also useful. Storage and redisplay of the same polygon (using the same XPLT) on another well's data reinforces previously recognized patterns.

* Polygon isolators drawn on the screen around patterns recognized by the user (left side of [[:file:log-analysis-applications_fig6.png|Figure 6]]) identify the enclosed data in the database for future reference. A dual XPLT/TPLT screen display of the same data (Figure 6) allows XPLT pattern recognition and isolation, and TPLT depth identification (usually with tic marks or color shading at the corresponding TPLT depths). The reverse procedure (TPLT depth interval isolation and XPLT identification) is also useful. Storage and redisplay of the same polygon (using the same XPLT) on another well's data reinforces previously recognized patterns.

* Chart overlays (only available for certain data combinations), relating wireline data to known lithologies and total porosity.

* Chart overlays (only available for certain data combinations), relating wireline data to known lithologies and total porosity.

* Statistical and user-drawn best fit lines and/or curves.

* Statistical and user-drawn best fit lines and/or curves.

* Three-dimensional plots, allowing rotation of the XPLT around the ''x, y'', and ''z'' axes. This usually requires specialized high performance graphics hardware.

* Three-dimensional plots, allowing rotation of the XPLT around the ''x, y'', and ''z'' axes. This usually requires specialized high performance graphics hardware.

[[file:log-analysis-applications_fig5.png|thumb|{{figure number|5}}A Pickett plot allow users to interactively draw a line intersecting water wet points (''S''_w = 100%). This line identifies the cementation exponent (''m'') and the product of a × ''R''_''w'' (empirical constant × formation water resistivity) and relates water saturation (''S''_w) to porosity and true resistivity.]]

[[file:log-analysis-applications_fig7.png|300px|thumb|{{figure number|7}}A two-well histogram allows users to compare data interactively from one well to another by shifting the second well's data across the base well on the screen. A visual best fit is usually satisfactory for determining the amount of normalization required.]]

 

[[file:log-analysis-applications_fig6.png|thumb|{{figure number|6}}Dual plot contains crosspiot (featuring data isolator polygon) and traceplot. User interactively draws polygon on the screen, which identifies the enclosed data in the database. Corresponding depths are immediately marked on the traceplot, in this case with tic marks.]]

===Histograms===

===Histograms===

''Histograms'' (''HISTs'') plot a trace's data values against their frequency of occurrence (Figure 3), showing the distribution of data across its range of values. A display of data from two wells on the same HIST (Figure 7) allows users to observe significant data node shifts between the two. Exact values of shifts can be determined by interactively moving data from one well across the other until a visual “best fit” is achieved.

''Histograms'' (''HISTs'') plot a trace's data values against their frequency of occurrence ([[:file:log-analysis-applications_fig3.png|Figure 3]]), showing the distribution of data across its range of values. A display of data from two wells on the same HIST ([[:file:log-analysis-applications_fig7.png|Figure 7]]) allows users to observe significant data node shifts between the two. Exact values of shifts can be determined by interactively moving data from one well across the other until a visual “best fit” is achieved.

 

The ability to combine data from many wells into a single composite XPLT or HIST allows the user to see at a glance the entire range and distribution of the data for any trace. Annotations on TPLTs, XPLTs, and HISTs are extremely useful when preparing displays for presentation and reports.

The ability to combine data from many wells into a single composite XPLT or HIST allows the user to see at a glance the entire range and distribution of the data for any trace. Annotations on TPLTs, XPLTs, and HISTs are extremely useful when preparing displays for presentation and reports.

===Advanced graphics===

===Advanced graphics===

Most of today's specialized high technology tools have developed specialized graphic displays of both raw and processed data. Understanding both the derivation and the presentation of the data is essential to understanding and interpreting the information presented. Examples include borehole imaging and dipmeter data (see “[[Borehole imaging devices]]” and “[[Dipmeters]]”).

Most of today's specialized high technology tools have developed specialized graphic displays of both raw and processed data. Understanding both the derivation and the presentation of the data is essential to understanding and interpreting the information presented. Examples include [[Borehole imaging devices|borehole imaging]] and [[dipmeter]] data.

==Data processing and output==

==Data processing and output==

* [[A development geology workstation]]

* [[A development geology workstation]]

* [[Introduction to contouring geological data with a computer]]

* [[Introduction to contouring geological data with a computer]]

* [[Two-dimensional geophysical workstation interpretation: Generic problems and solutions]]

* [[Two-dimensional geophysical workstation interpretation: generic problems and solutions]]

==References==

==References==

[[Category:Integrated computer methods]]

[[Category:Integrated computer methods]]