TOC Analysis

Purpose

Total organic carbon, Total organic carbon, answers how rich in organic matter the rock is. For an unconventional reservoir it is the first measure of source-rock and resource quality, and it controls how much hydrocarbon the rock can hold as adsorbed and free gas or oil. For any shale it is also needed to make the porosity and mineral volumes correct, because kerogen is much less dense than the minerals around it and disturbs every log that responds to density.

Position in the workflow

Upstream. TOC needs clean, normalized logs from Stage 1. Density-based methods are damaged by washouts, and resistivity-based methods by bad resistivity data. The washout flag and the repaired logs matter more here than in a conventional reservoir.

Downstream. TOC is converted to Kerogen volume, which then feeds:

the porosity calculation, where the low density of kerogen has to be removed before density porosity is read as pore volume,
mineral inversion, which takes the kerogen volume as an input, and
resource calculations, where the organic volume and its maturity control adsorbed and free hydrocarbons.

Error propagation. A TOC that is too high removes porosity through the kerogen correction, and one that is too low leaves the organic matter in the porosity. Because kerogen volume is roughly twice the weight fraction, an error of 1 wt% in TOC is about a 2.5% error in volume, which is comparable to the whole porosity of a tight shale.

Key concepts

Weight and volume. TOC is a weight fraction of carbon, normally from core analysis. Porosity and inversion work in volume fractions, so TOC is converted to kerogen volume with the density and carbon fraction of kerogen. See Kerogen Volume and Maturity.

Log signature of organic matter. Organic matter has a low density, a high slowness, a high neutron response and, when it has generated hydrocarbons, a high resistivity. TOC methods use one or more of these.

Two families of method. Density-based methods (Schmoker, Vernik) treat the rock as a mixture of kerogen and an inorganic matrix. Separation methods (Passey, Faust) compare a resistivity curve with a porosity curve against a non-source baseline.

Maturity. The resistivity response depends on how much organic matter has turned into hydrocarbon. A maturity measure, Level of organic maturity derived from Vitrinite reflectance, is needed by the separation methods.

Calibration to core. No log method measures TOC. Every one is a regression against core TOC, from pyrolysis or combustion, and uncalibrated results are only qualitative.

Method selection guide

Method	Inputs	Use when	Strengths	Weaknesses
Passey ΔlogR	Resistivity, plus sonic, density or neutron; baselines; LOM	A resistivity curve and a porosity curve are available, and the interval is not a hydrocarbon reservoir	Widely used and well understood, three porosity options	Needs baselines and maturity, fails in porous reservoir intervals, weak in immature and overmature rock
Schmoker	Density; two fitted coefficients	Only a density log is available, or an independent check is needed	Simple, no resistivity needed	Reads pyrite, porosity and washouts as TOC; coefficients must be fitted
Vernik	Density; inorganic shale density; kerogen density	A representative inorganic density can be picked or computed	Physically based, no fitted coefficients	Sensitive to the inorganic density; same density-log weaknesses
Faust	Sonic, resistivity, depth; multiplier	A sonic and resistivity are available and core allows a fit	Uses a sonic-resistivity reference	Confused by overpressure and gas; needs calibration
Combining	Two or more of the above	Several estimates are available	Reduces the effect of any one log's errors	Not independent if estimates share an input

Decision guidance

If you have a resistivity curve and a good porosity log, start with Passey and compare with a density-based estimate.
If you only have a density log, use Vernik if you can pick an inorganic density, and Schmoker if you have core to fit the coefficients.
In pyrite-rich or heavy-mineral rock, density-based methods overestimate TOC, so give more weight to Passey.
In porous, hydrocarbon-bearing reservoir intervals, avoid the resistivity-based methods.
If no core is available, treat all results as relative, not absolute.

Shared parameter picking

Baselines for the separation methods. Resistivity baseline and the porosity baseline (Sonic baseline, Density baseline, Neutron baseline) are read at one depth in a thick organic-lean shale where the curves overlay. They are shared by every variant of the Passey method, so pick them once per zone.

Source-rock cutoff. A minimum gamma ray (Minimum GR for source rock) is often used to set TOC to zero in clean, non-source intervals such as sandstone and limestone, where the density and resistivity responses are not from organic matter.

Maturity. Level of organic maturity, from Vitrinite reflectance, is shared by all separation methods.

Kerogen properties. Kerogen density, Kerogen carbon fraction and Matrix density are used by the density-based methods and by the weight-to-volume conversion. Use one set across a project.

Calibration to core. A scaling factor (TOC scaling factor) and a shift (TOC shift) applied to a computed curve are the simplest way to match core TOC. They are shared by every method and are set after the other parameters.

Recommended default approach

Absent other information, a careful generalist would:

Flag the source-rock interval with a gamma ray cutoff and set TOC to zero outside it.
Pick the baselines in an organic-lean shale and compute the Passey ΔlogR with the best porosity log.
Compute one density-based estimate (Vernik or Schmoker) as an independent check.
Calibrate both against core TOC, using the scaling factor and the shift.
Combine them with the mean if they agree, and investigate where they do not.

Combining methods

Averaging independent estimates reduces the error of any one log. The median is a safe default with three or more estimates, and the minimum is conservative when the main errors only push the result up. The estimates must be calibrated first, and estimates that share an input are not independent. The details are on the combining page. A fit to core by regression is the alternative, when enough core is available.

QC of results

A good result:

is zero or close to zero outside the source interval,
compares with core TOC with no strong bias and an acceptable scatter,
agrees in trend between the separation and the density-based estimates, and
does not follow the caliper or the pyrite-rich intervals.

Signs of a bad result: TOC in clean sandstone or limestone, spikes in washouts, TOC that rises in a hydrocarbon-bearing reservoir, and a constant offset from core.

Common pitfalls

Picking the baseline in an organic-rich interval.
Applying a resistivity-based method in a porous, hydrocarbon-bearing reservoir.
Ignoring maturity in the separation methods.
Reading pyrite, heavy minerals or washouts as organic matter in density-based methods.
Using TOC weight percent where a volume is needed.
Using different kerogen densities and carbon fractions in different steps.
Skipping calibration to core.

Going Deeper

Methods that estimate organic richness from logs date from the late 1970s, with density-based relations for Devonian shales, and the resistivity-porosity overlay methods came in 1990. Since then the field has moved toward regression and machine-learning models fitted to core, which learn the combination of logs that best predicts TOC for a formation. Those models are only as good as the core they are fitted to and do not extrapolate well to other formations. The oldest unresolved difficulty is that every log response to organic matter is also a response to something else: porosity, pyrite, gas, compaction or hydrocarbons.