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Phil Holbrook Ph.D.
Consultant Scientist



Scientist Force Balanced Petrophysics

2203 Blue Willow Dr.
Houston, TX, 77042

mobile: 832-423-4577





These two books are a significant advance in physical science that now includes the mechanics
inside of our earth.


Order Your Copy Today!

Deterministic Earth
Mechanical Science

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Pore Pressure through Earth Mechanical Systems
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You can get any number of copies at the retail price from Amazon.com

Copies can be found by entering
the unique ISBN's found below.

Deterministic Earth
Mechanical Science
ISBN: 0-9708083-3X

Pore Pressure through Earth Mechanical Systems
ISBN: 0-9708083-2-1



Force Balanced Log Services



Drilling Reservoir and Geomechanical Services are available as continuous trace logs and related interpretive advice. The logs are dependant on petrophysical input data. The separate technologies are inter-dependant through their common dependence on [mass-energy] and [effective stress/strain] conservation laws.

Near mudline soil mechanics including compaction are related to sediment acoustic properties through the Extended Elastic Equations of Hooke's law.

Rock Properties
Porosity, Permeability, Shale Volume, Quartz, Calcite, Anhydrite, Halite, Basic Water Saturation, Lithostratigraphic Sequences

Initial Shut-in pressure,
side load,
Rock properties
Table 6.1 to 6.5 Summary of
related rock
pore pressure
and loads.



For a detailed explanation of the single well log calibration procedures and explanation see log calibration and the safe drilling window in "The use of petrophysical data for Well planning, Drilling Safety, and Efficiency". Below is a synopsis of the regional aspects of well planning log calibration in the deep water Gulf of Mexico.


For more explanation, hyperlink to the AAPG extended abstract, Pore Pressure Prediction and Detection in Deep Water.   RETURN TO TOP

Rock properties calculation flowchart for different petrophysical sensors



Rock properties have been related directly to the mineral and fluid composition of sedimentary rocks. The panel above shows the means of determining True Rock

from each of three petrophysical sensors. Each sensor specific transformation necessarily depends on an estimate of shale volume and the dominant non-clay mineral, quartz or calcite.

Gross lithostratigraphic sequence type [(calcite-clay) or (quartz-clay)] is generally known from external means. Given this sequence type guidance, the sensor specific flowpaths shown above should converge on the best answer for True Rock Porosity.

Holbrook, P W, D A Maggiori, & Rodney Hensley, 1995b, "Real-time Pore Pressure and Fracture Pressure Determination in All Sedimentary Lithologies",pp 215 - 222, SPE Formation Evaluation, December 1995 ( selected for SPE reprint series) contains more specific information on how all these sensor-specific transforms operate.

For information about rock properties services, you can contact Phil Holbrook.  RETURN TO TOP

Lithostratigraphic Sequence Type correspondence to Effective Stress/strain relationships.

serviceMost lithostratigraphic sequences fall into 1.) Quartz grainstone - claystone; 2.) Limestone-claystone; or 3.) Salt evaporite; categories.

Salt (Halite) usually has zero porosity above 300 psi effective stress and has sharp contacts with surrounding lithotypes. It needs to be separated from the other lithotypes through petrophysical guidance.

Limestone-Claystone-Quartz lithologic mixtures usually occur in one of two lithostratigraphic sequence types. The dominant factor affecting the natural occurrence of lithostratigraphic sequence types is the average seafloor water temperature. Subtropical to tropical water temperatures favor the precipitation and preservation of calcite. This leads to limestone-claystone lithostratigraphic sequence types shown on the LIMESTONE-CLAYSTONE face of the ternary composition triangle .

Colder water temperatures tend to dissolve calcite. Quartz grainstone-claystone lithostratigraphic sequences dominate where calcite is not chemically stable. Given some type of local lithostratigraphic sequence type knowledge, the dominant mineralogy of the low gamma ray lithology can be set to QUARTZ-CLAYSTONE. Given this small amount of operator guidance, effective stress can be calculated from porosity along the lines shown on the Lithostratigraphic Sequence Type ternary diagram .

Rock properties from the Extended Elastic Equations mechanical system and rock load state data from grain-matrix-compactional mechanical systems can be applied simply and directly to solve drilling, reservoir, and completion subsurface engineering problems. These parameters are in the correct units to be used directly in geomechanical applications.   RETURN TO TOP

Quantitative Rock Properties and Loads output for
each foot of petrophysical data

Table-6.1. Constitutive rock or sediment properties
1.) Porosity 2.)Permeabilty; Solid volume fractions of, 3.) Clay minerals 4.) Quartz 5.) Calcite 6.) Halite

Table 6.2 Whole rock and density properties
7.) Bulk modulus 8.) Shear modulus 9.) Young’s modulus 10.) Bulk density 11.) Compressional wave velocity 12.) Shear wave velocity 13). Dry rock Poisson’s ratio

Table-6.3 NaCl brine properties
14.) Electrical conductivity 15.) Density 16.) Compressional wave velocity

Table-6.4 Rock confining Load, Pore pressure and Effective stress data
17.) Pore fluid pressure 18.) Overburden = vertical load 19.) Fracture propagation pressure = minimum horizontal load 20.) Average effective stress 21.) Vertical effective stress 22.) Maximum Horizontal Effective stress

Table-6.5 Regional temperature related profile data
23.) Geothermal gradient 24.) NaCl brine conductivity 25.) Dry Clay mineral grain density profile

Reservoir Management using deterministic force balanced principles
by Phil Holbrook Ph.D.


Reservoir management involves seismic imaging, petrophysics, fluid mechanics and rock mechanics. These disparate technological specialties combine seamlessly when each is based on mechanical first principles. Reflected seismic waves provide images of reservoirs. The wave trains also contain a great deal of presently un-used rock property information.

Elastic wave refraction, transmission, and reflection involve Hooke’s law and Snell’s law. Conventional seismic data processing follows the governing wave physics in large part. However, the rock properties relationships of these same laws are generally not included in seismic data processing.

Wave and rock constitutive physics are mechanically related in every finite boxel element in the earth. There are obvious advantages to be gained by combining first principle physics in these finite boxel elements. Information on porosity, loads, stresses, and pore pressure are contained in the reflected wave train. This information can be extracted and used at the proper scale in mechanically guided reservoir management.

Comparable information is contained in borehole measured petrophysical data on a much finer scale. The same governing physics applies at both wavelength and sample interval scales. Data from either source are naturally constrained to sum to the same transit-time, average lithology, density, and elastic coefficients by physical boundary conditions.

Disparate data source equalization is logical and valid in closed mechanical systems domains. Equalization is not logical and is not necessarily done in empirical reservoir management schemes. Properly constrained large- and small-scale rock property information can and should be used together in reservoir management.

Reservoir simulators operate through Darcy law fluid flow through solid mechanical constraints. The quality of many reservoir simulators is open to question. I have read several published reservoir simulators that reversed the effects of solid and fluid compressibility. Unexplainably, this resulted in a "successful" history match. The match may mean that some other gross error or errors are compensating for the known error incorporated in the simulator model.

Reservoir rock compressibility due to fluid pressure drawdown is presently extrapolated from laboratory data. The time scale and physical scale of laboratory experiments are far from that of the reservoir. Lab experiments need not stand alone to cover this critical reservoir performance gap. An in situ grain-matrix compactional mechanical system can provide a much needed boundary condition framework for the small-scale short-duration laboratory data.   RETURN TO TOP

The textbook "Pore pressure through Earth Mechanical Systems", relates loads, stresses, and fluid pressure to rock physical properties porosity, mineralogy, and pore fluid. This is a corresponding geologic time scale closed-form earth mechanical system. Log calculated mechanical system parameters are on a 1-foot scale that is comparable to that mechanically determined in a laboratory. Spatial scaling and time scaling can both be realistically extrapolated from the sparse laboratory data.

There are issues of three different time scales of stress/strain behavior to be addressed in bringing laboratory, in situ elastic and grain-matrix compactional mechanical systems together. However, if all three systems are related to mechanical first principles; the time and scale issues can be resolved.

Borehole and laboratory mechanical systems parameters can also be integrated up to the scale of a reservoir simulator box. The box contents of porosity, mineralogy, compressibility, and permeability would then be related to fluid and solid mechanical systems principles. Fluid and solid are mechanically coupled in the reservoir. Boxel content data regulating Darcy law flow, would also contain coupled solid-fluid mechanical systems data. It would not be possible to reverse solid and fluid compressibility in a simulator that is dependent upon real mechanical systems data.

The preceding mechanical systems discussion covers only first order effects. There is a second tier of mechanical systems relationships that can be related to 4-D behavior of a reservoir. In nature the second order stress/strain relationships are consequent to and dependant on the first order relationships. Using mechanical systems, second order effects can be realistically added.

Forced-fit empirical relationships are based upon observations of coincidence. In an empirically dominated reservoir simulator there is no need for temporal or physical inter-dependence. Empirical forced-fit relationships are probably not involved in today's reservoir simulator models. Empirical forced-fit relationships are not physically constrained nor need units match. Even the impossible seems to be possible when you accept non-physical conditions.

Mechanical systems are deterministic. Measurement units match within a deterministic mechanical system domain. Units and measures transfer to other mechanical systems domains. These relationships are causal, not accidental. Using existing mechanical systems is simpler and more reliable than attempting to construct empirical forced fits. As the number of extraneous coefficients increases, the work involved and the certainty of forecast results decreases.

In summary, physical laws govern the entire process involved in reservoir management. There are probably less than a dozen physical laws involved. Each law will reliably relate one borehole or petrophysical measurement to another or several others. The governing physical laws are related to each other algebraically and through physical rock property coefficients. If mechanically deterministic first principles are employed to the maximum, uncertainty will be reduced to the minimum.

There is wisdom in relating the performance of a valuable reservoir asset to the controlling physics. I feel fortunate to have constructed a grain-matrix loading and un-loading mechanical system; and completed Hooke’s law dynamic mechanical system for sediments and rocks. Both are built from mechanical first principles.

Reservoir assessment and reservoir mechanics are related to and fall within these mechanical systems domains. I offer my assistance as a consultant and teacher in these matters. If you are interested, Contacct Phil.      RETURN TO TOP