For the regional efforts presented here two sources of raw wireline data were utilized. The first set of data was chosen from a non-public wireline database where any well in the Powder River Basin having a triple combo log suite (i.e., GR, RDEEP, NPHI and RHOB) was filtered and utilized in our efforts. The second set of data utilized was accessed from the Wyoming Oil and Gas Commission -WYOGC [8] by asking for the digital records of the wireline logged wells in the basin. Here again, wells utilized in this study were filtered for depth (wells with the most significant continuous logged coverage) as well as a minimum of a GR (gapi), RDEEP (ohms) and NPHI (dec) were utilized. These were the base wireline suites required however we note several wells did include PE (b/e), DT (us/ft), and some advanced wireline logs for our integrated efforts.

All raw wireline logs were quality controlled for borehole washout, casing points, poor log quality due to stick/slip. A borehole quality flag based on a DRHO threshold of 0.2 ≥ DRHO ≤ −0.005 was generated for use and often aids the petrophysicist and geologist by visually demonstrating where wireline data might not be able to be used for property assessment (Figure 8). In addition, all raw wireline logs were environmentally corrected for logging vendor variation, tool vintage and lithological scale. For this study, all neutron porosity (NPHI) logs were set to a limestone end member matrix scale assuming limestone equals 2.71 g/cc (Figure 9).

As the Powder River Basin log response is challenged by the lithologic presence of coals and volcanics, additional flags were generated to identify these wireline responses in the stratigraphic column. The flags can be utilized by a geoscientist wanting to then map petrophysical properties varying the accuracy of the VCL (v/v), PHIT (%) and SWT (%) statistical values. For example, in the Mowry, a geoscientist may not want to include the interpretation of VCL, PHIT and SWT of the volcanics in the section as this will create a bimodal distribution of these properties and therefore inaccurately represent the shales average properties. Flags generated here (known as COAL_FLAG and VX_FLAG) can be used as cut-off values and applied to regional or local-scaled mapping efforts (see Figure 5).

· COAL_FLAG = 1 where NPHI ≥ 0.35 and RHOB ≤ 2.4 from 0 - 5000 MD’ ELSE 0

· VX_FLAG = 1 where RHOB ≤ 2.45 from 4000-TD MD’ ELSE 0

Finally, raw wireline log responses were utilized as an independent way to then interpret the geological context since the basin wireline coverage could be infilled with wells containing just a gamma ray (GR) and neutron porosity (NPHI) log etc. bypassing the need for a triple combo suite to execute a full petrophysical evaluation. Raw wireline averages applied cutoff summations and footage of cutoffs were utilized to highlight depositional and property-based trends that are then interpreted in the context of the basin history. These wireline maps also supplement the petrophysical evaluation since any given property is a derivation of the raw digits. The effort is an effective quality control check on the petrophysical evaluation principles as we derive the properties from these log suites. For example, applying cutoffs or mapping bins of GR ranges, we can interpret those maps as net sandstone, net silty-sand and net shale representative maps. Deep resistivity cutoffs applied for these efforts include the footage of deep resistivity (defined by > than 90-inch investigation radius) RDEEP ≤ 8, 6, and 4 ohms. These cutoffs can be interpreted to represent variation in low resistivity, low contrast pay potential as well as what has increased water-filler versus hydrocarbon-filled pore volume. Therefore, when mapped can highlight the proximal to distal trends of the stratigraphy. As well as where low resistivity, low contrast pay can be observed. Raw wireline cutoffs were also utilized for bulk density (RHOB) and sonic/acoustic (DT) (supplements by pseudo-DT logs) to represent trends in total storage as well as lithology and the overpressure presence in the basin (Figure 10). Petrophysical wireline-based interpretation across a regional set of wells results in our ability to map rock properties and provide hydrocarbon system context to disciplines. Below is an example of how a petrophysical evaluation in the Powder River Basin can then be mapped in 2D space for a given formation and property of interest (Figure 11).

Debate within the petrophysical discipline is still prevalent regarding the approach and application of normalized log efforts. For this study, we focused on the normalization of the GR and RHOB raw wireline logs as a deliverable for the geologist or stratigrapher correlating well tops across the basin. As such, a single point bulk shift normalization effort was conducted with the end member well being chosen based on basin position and log suite available. Below we demonstrate the impact graphically as well as in wireline format of the normalization effort (Figure 12 and Figure 13). Post normalization, these logs were not utilized in the following petrophysical evaluation efforts presented here. The justification being the potential impact in muting or skewing any true geological end member changes in the basin.

In any given basin investigated petrophysically there will always be some scarcity of coverage regarding the wireline data available or vintage etc. There is no exception in this case study presented here. While we attempted to include as complete of raw wireline log suites as possible, we did need to generate log curves that are a derivative of a correlation to another log that is more commonly measured regionally. For example, there is a lack of monopole and dipole acoustics in the PRB according to our ability to search and utilize log suites in our efforts. The same can be said for the presence of photoelectric (PE) logs.

As such, pseudo correlation for these logs specifically were generated. A correlation of photoelectric factor (PE b/e) to bulk density (RHOB g/cc) was generated and therefore a pseudo-PE utilized in a few wells to allow a multi-mineral model approach to be executed. See correlation below (Figure 14).

In addition, correlation between deep resistivity and neutron porosity can be established for wells that lack a raw measured wireline based NPHI (dec) (Figure 15). Neutron porosity is a critical input to multiple petrophysical property derivations. In leu of a lack of physically measured logs we can apply the pseudo-correlation as established for a best fit correlation and go from there. Of course, any time we are honoring a regressive analysis through a distribution of data, the resulting pseudo-curves lack the entire spectrum of variability that does exist geologically. The end members of which are not captured in the pseudo-log application. However, much of the rock property distribution is honored and maintained as valid and represented through the stratigraphic column. Additional efforts were focused towards establishing a more robust multi-linear regression correlation and solution. However, the impact to the predictive pseudo-curves generated was minimal and therefore not justified versus the business case uplift. If a client would like additional work to be performed applying a more advanced statistical approach to infill data holes and generate/establish curve relationships (i.e., Random Forest, Clustering, Self-Organized Maps, Non-Linear Curvature etc.).

Likewise, to extrapolate the sonic monopole and dipole DT, DTC and DTS log presence, first a correlation of DT/DTC was observed and generated. Next, the correlation between DTC to DTS was generated. Thereby allowing us to generate a pseudo-DTC and DTS (Figure 16 and Figure 17). This effort was focused on to provide the necessary inputs to utilize acoustics for the following properties. For example, acoustic based porosity on a carbonate and sandstone end members, identification of top overpressure, pore pressure prediction, rock typing electrofacies variations and geomechanical properties.

In this study, raw wireline-based electrofacies models were developed following the below methodology. Due to the lack of acoustics and additional advanced logs suites in the basin either publicly or for purchase now, the electrofacies models developed focused on triple combo inputs including. Including GR (gapi), RDEEP (ohms), RHOB (g/cc), and NPHI (dec). A series of unsupervised models were developed using hierarchical cluster statistical logic with the minimum of the least square’s regression distance applied to reduce probabilistic overlap in the cluster association. No discriminators were applied and no petrophysically derived properties were input to train the model.

Cluster-based models were run allowing for identification of 20, 18, 16, 14, 12, 10, 8 and 6 overall populations therefore resulting in electrofacies rock type associations that correspond. The deliverables can be utilized by geologists to readily identify major, as well as minor, changes in the wireline signatures across the stratigraphic column. Stratigraphic tops can be correlated based on these models. Geological maps can be generated as well, however would require interpretation of what each rock type tied to a sedimentological or structural context means (i.e. Bioturbated siltstone versus organic-rich mudstone). Without the additional environmental context leading to that interpretation, the electrofacies can be used to communicate observed changes in the triple combo wireline set and context provided in that way. For example, electrofacies A has high GR, high RDEEP, low RHOB and therefore could be interpreted as a “shale” (Figure 18 and Figure 19).

The volume of shale is an interpretation derived with the assumption that the mineralogy of the stratigraphy being invested is binary. Meaning the stratigraphy is either sand or shale with no deviations. That assumption is often valid in many conventionally targeted reservoirs and does therefore apply to the shallower historic legacy targets in the Powder River basin (i.e., Lewis Shale, Teckla, Teapot, Parkman, Shannon, and Sussex formations). As such, a volume of shale interpretation was derived in every well of this study and extrapolated across the stratigraphic column (Figure 20). The curve should only be applied for mapping purposes where applicable.

The volume of shale can be interpreted by the single curve use of GR, SP, RDEEP, NPHI or DT wireline logs where a baseline of end member shale and baseline of clean sand are determined. Or logs can also be used in combination such as NPHI and RHOB after Thomas and Steiber, 1975. In this study, we used a deterministic approach where only the GR was utilized for the interpretation. A clean sand baseline of 45 gapi was applied and an end member shale baseline of 180 gapi. Then the following equation is applied, and the volume of shale interpreted. We assumed a linear relationship between depth, GR value and the volume of shale, no Clavier or formation age dependent correction was applied.

Volume of Shale (Vsh) = GRfromlog − GRminimum/GRmaximum − GRminimum

The volume of clay is a critical interpretation solved for independent of the multi-mineral model applied in this study. Multiple approaches were taken to calibrate and constrain the resulting extrapolated VCL interpretation across the basin. These methods include 1) the volume of clay correlated to spectral gamma ray thorium (ppm) data where present; 2) An interpretation that is derived from gamma ray after calibrating baselines for the shale versus clay end members 3) An interpretation based on the neutron porosity and bulk density separation method (1/3 - 2/3 rule implied) (Figure 21). Neutron logs must be scaled by the given matrix they are relative too. A petrophysicist must ensure utilizing the interpreted neutron log on a limestone versus sandstone matrix accordingly. Is appropriately corrected and end member lithology matrices applied, the log by itself can be utilized for a total fluid-filled porosity estimate (note that the fluid medium can impact the log itself, for example brine versus hydrocarbon). When the neutron log moves to lower neutron values, this is often indicative of a carbonate lithology with little to no fluid-filled porosity. When the log increases this is often indicative of a sandstone or shaley-sandstone with fluid filled porosity present. The log is typically used in combination with a density porosity for an integrated interpretation (Figure 22).

To aid in calibrating and constraining methods 2 and 3 noted above, the neutron-density cross-plot approach was applied and measured x-ray diffraction result for the weight of total clay volume were employed. Variation in wells where a gamma ray, neutron and density were available to utilize for interpretation were worked individually to cross-check independent solutions. In the case where all curves were a present, they were all utilized by taking the minimum solution for the final VCL determination. The VCL curves were then used as an independent input into the multi-mineral model (Figure 23).

Clay speciation is also important to note, and notes vary throughout the basin. Most important to note is the presence of chlorite in portions of the stratigraphy and basin that is otherwise Illite-mica dominated. There are spatial trends in the clay specification as related to the time-temperature history and therefore conversion of Smectite to Illite in the basin. The speciation is of importance to petrophysics to ensure the grain densities associated with the clay volume are accurate. Heavier clays, such as chlorite, can have impact to the calibrated clay and therefore multi-mineral model which in turn impacts the calibration of the total storage and other properties in the evaluation.

Thermal maturity integration can be done by a few techniques to then interpret a wireline based continuous maturity log. Here is an example from the Powder River basin where a cross-plot of measured Vro versus depth and colored by county displays two distinct trends (Figure 24). The same can be done for a correlation between measured vitrinite reflectance (Vro) and thermal maximum from the S2 (mg/g) peak as defined by programmed pyrolysis measurements (TMAX). The defined functions can then be used in wireline interpretation. The assumption with any correlation such as this, is that the stratigraphy did not experience any significant uplift events through time and that a simple depth to burial to thermal maturity relationship is valid.

TMAX as a VRo equivalent can also be calculated continuously along the length of a stratigraphic section derived from correlations to the source rock dataset. In this study an association of total organic carbon (wt%) (TOC) correlated with programmed pyrolysis S1 (mg/g) was established (Figure 25):

S1 = 0.3851 * TOC OR S1 = 0.1042E0.8358x

Then

S2 = 0.2589E1.2268 * (TOC)

Then

TMAX = 431.273 + 3.63636 * S2

And Finally

JARVIE_VROEQ = TMAX * 0.018 − 7.16

Both applied interpretations provide the petrophysicist with the maturity estimate needed to estimate hydrocarbon phase (oil versus gas) as well as constrain inputs for other property evaluations (ex. Level of Metamorphism) [9]. The maturity estimates that can be derived allow the geologist to generate 2D maps for any stratigraphic horizon and flex to their defined isopach in include the impact of structure and therefore stratigraphic thickness variation on the log-derived estimates presented here. The log-derived solutions will also allow a comparison between in situ mapped maturity and formation produced gas- oil-ratios.

Total Organic Carbon determination is critical to the petrophysical evaluation of an unconventional play. Wireline based total organic carbon interpretation is step 2 of the overall petrophysical workflow (second to interpretation of the volume of clay). In unconventional plays, the determination of total organic carbon (TOC wt%), can be conducted by several different calculations. Schmoker’s Law/Principle is one such relationship where the correlation of bulk density (RHOB g/cc) to measured total organic carbon can be utilized [10] (Figure 26).

SCHMOKER_TOC = 51.3291 − 19.3217 * RHOB (Bulk Density g/cc)

An alternative method from Schmoker to interpret Total Organic carbon (wt%) relies on Passey et al.’s principle [9 , 11]. When formation resistivity and sonic are linearly scaled relative to one another, the degree of resistive deflection off an acoustic baseline represents the degree of the log of resistivity difference (DlogR) between 0 to an associated total organic carbon quantification. There is an impact of maturity on the degree of deflection, and this is considered through the level of metamorphism constant that is assumed when the Passey’s method is applied [9]. The method can also be applied relative to neutron porosity or bulk density. An empirical relationship between maturity and sonic logs can also be utilized. An example of a wireline based total organic carbon interpretation via Passey’s DLOGR (1990) approach with cuttings LECO^® total organic carbon (TOC) plotted on top for calibration is below (two different wells for reference, Figure 27).

In this study the following criteria were used in the below order to expand total organic carbon interpretation as extensively as possible extrapolating away from known constraint and assess uncertainty in the extrapolated solution (Figure 28):

1) If RDEEP, DT, RHOB and NPHI present utilize all curves for DLOGR_TOC

2) ELSE Use RDEEP, DT and RHOB

3) Else Use RDEEP, DT OR RHOB dependent on which is present

The analysis along a wellbore was also baselined by subdividing on the following stratigraphy where significant change required a baseline change in the DLOGR approach:

1) Lewis Shale Baseline

2) Teckla through Mowry Baseline

3) Muddy to TD Baseline (includes Dakota and Minnelusa where present)

Resulting final curve is PASSEY_TOC

where

· LOM ranges from 9.8 - 11 (stratigraphy dependent)

· DT offset = 0 (stratigraphy dependent)

· RHOB offset ~0.215 ranging up to 2 in the Lewis Shale (stratigraphy dependent)

· NPHI offset ~−0.594 (stratigraphy dependent)

Programmed pyrolysis data is utilized by petrophysics to correlate interpreted total organic carbon with S1, S2 and TMAX constraints. S1 is used as a direct hydrocarbon indicator and when compared to mineralogy, porosity and saturation is a good approach to obvious “pay”. S2 (utilized as remaining generative capacity by comparing the as received versus extracted measurements), correlates with total organic carbon as well and can be derived then by wireline-based interpretation (Figure 29). TMAX correlates with Vro and often Vro correlates with depth (Figure 30). So TMAX can also be derived via wireline logs. All utilized in estimates of wireline based thermal maturity, source rock quality and direct hydrocarbon indicators. The S1, S2 and TMAX continuous logs can then also be utilized to approach “oil mobility” from the integration with geochemistry (Figure 31).

General Trends:

As mentioned previously in the “Maturity” section of this study, correlations between the total organic carbon (wt%), S1 (mg/g), S2 (mg/g) and therefore TMAX (degrees C) were established. The equations were applied to all wells where wireline logs existed to derive the interpretation of the programmed pyrolysis geochemical parameters (Figure 32).

Here we utilize the S1 g/cc as a direct HC indicator and replace the classic use of a resistivity dependent total water saturation to then calculate an OOIP. The use of S1 since it is an in situ reflective measurement also bypasses the need for a Formation Volume Factor input (Figure 33).

where OOIP/640 = (4965.36 * pb * S1 * pOil * 0.5’) [20]

Assume pOil = 0.85

Curve = OOIP_GEOCHEM

In addition, the correlation between total organic carbon (weight %) and programmed pyrolysis S2 (mg/g) can be established based on the cross-plot below (Figure 34). From the cross-plot the derivation of a continuous log-based S2 is established and provided as part of the derivative products for this study. Lastly, the correlation between S2 (mg/g) and TMAX as well as TOC (wt%) versus TMAX was established. A continuous TMAX log-derived curve was interpreted.

From these derivative curves Jarvie’s equation [12] was applied.

VROEQ curve:

where VROEQ = TMAX * 0.018 − 7.16

The above VROeq estimate can then be compared to additional maturity estimates derived from oil-based interpretations, vitrinite reflectance measurements as well as any association of known produced gas-oil-ratio to in situ maturity.

Two Ways to Approach a Petrophysical Model:

1) Deterministically

2) Probabilistically (Inversion)

Deterministic: IF GR ≤ 45 GAPI then Carbonate

Inversion: Based on multiple inputs (GR, RHOB, NPHI, PE, VCL and TOC) solve for Carbonate, Sand and Dolomite based on the ranges/assumptions given.

Multi-Mineral Models are typically inversion-based. The benefit of a multi-mineral model is that at every 0.5 foot increment the mineralogy is resolved for the most accurate grain density representation based on the mineral model applied. Rather than assuming a constant mineral matrix (for example 2.65 g/cc), the multi-mineral model is solving for that matrix input variably across the applied model interval. Therefore, presenting a better representative mineralogy, total porosity, and total water saturation.

In this model the following minerals were resolved: Quartz, Clay (independently solved and therefore an input), Kerogen (converted from independently solved total organic carbon as an input), Limestone, Dolomite. The model was calibrated to available x-ray diffraction data (Figure 35). Uncertainty in total clay interpretation is ±5%. Uncertainty in total organic carbon interpretation is ±2%.

For this study total porosity was approached from multiple calculations including deterministic and inversion-based approaches. Multiple calculations are shared as a part of this study to ensure the scientist can investigate any portion of the stratigraphic stack including the conventional sandstones where an investigator would want to utilize the density porosity solution on a 2.65 g/cc matrix (DPHIT_SS) versus unconventional stratigraphy where the application of the multi-mineral model solution results in a variable matrix density on the 0.5 foot for total porosity (PT_PHIT) (Figure 36).

Resulting curves include:

Density Porosity (DPHIT) = Rhomatrix − Rhobulkdensitylog/Rhomatrix − Rhofluid

Sonic Porosity (SPHIT) = DeltaTlog − DeltaTmatrix − DeltaTfluid − DeltaTmatrix

· DPHI_SS: Assumes 2.65 g/cc

· DPHI_268: Assumes 2.68 g/cc

· DPHI_LS: Assumes 2.71 g/cc

· SPHI_SS: Assumes 55 us/ft

· SPHI_DS: Assumes 40 us/ft

· RhoFluid (Water-Based): 1 to 1.1 g/cc or 189 us/ft

· RhoFluid (Oil-Based): 0.85 g/cc

· PT_PHIT (P50): Does not use a constant matrix value. Derived from the multi-mineral model variable matrix solution.

The derivations allow for “quick-look” exploration basin analysis, down to detailed per stratigraphic zone calibrated development-scale evaluation. Overall, the Best Fit (otherwise defined as the P50 solution), is the PT_PHIT resulting from the calibrated multi-mineral model (Figure 37).

In addition, the correlation between total porosity and permeability in the major formations in the basin were established. Cross-plots of routine and crushed rock core analysis were constructed and utilized to derive the two porosity-permeability transforms that result and can then be applied to interpret a log-based continuous permeability curve. One curve derived from the relationship between routine core analysis (intact plug assumptions) measurements at net confining stress therefore corrected for slippage and reported as Klinkenberg results (KKLINK mD), “ROUTINE_PERM”. The second derived from the correlation of crushed rock analysis results “CRUSHED_PERM” (Figure 38). There is a lot of debate in the industry and amongst disciplines to define what the most “accurate” and representative permeabilities are for a formation. In this applied study we highlighted the way to approach or to provide the solution to both techniques with an understanding of their applications and challenges/limitations. The investigator must be cognizant of the solution they utilize as applied to the formation of interest.

Petrophysicists use water salinity and compositional data for many purposes. The application of the United States Geological Survey public water data [13] interpreted in this study for petrophysical derivatives and purposes did not consider the impacts of the basin history and therefore hydrodynamics beyond that of a recognition of water-injection into the Teckla through time. Water analyses were utilized at a high-level simply to constrain gross changes in trends of salinity varying outside of a ±20 kppm variance around a given mean in a map-based product. The raw data and maps were utilized to “guide” the petrophysical resistivity of water (Rw) regarding knowing that an adjustment might need to be invoked in then determining total water saturation.

1) Example below is a plot of total dissolved solids (ppm) in the Powder River Basin versus depth. The onset of increased salinity correlates to increases in maturity as well as top overpressure regionally (Figure 39).

2) The main purpose of water salinity is to constrain a critical input parameter known as Rw (resistivity of water) for total water saturation evaluation (SWT).

Petrophysicists use water salinity and compositional data for many purposes.

* Rw is a critical parameter to constrain because it determines where (based on resistivity) a petrophysicist will interpret a formation to be primarily oil- versus water-prone or filled.

* Particularly challenging are low resistivity, low contrast pay play types. These formations are where the Rw is lower than expected based on salinity and Pickett plot constraints and a petrophysicist can very easily mis-evaluate these plays as all water-filled, when, they have oil-prone potential.

Below is the equation to convert water salinity to formation resistivity given a known temperature gradient.

Resistivity of water = 0l0123 + 1/2.74 × 10⁻⁴ (NACL ppm equivalent)

An example of integrating total dissolved solids (TDS in ppm) data in the Powder River Basin in a map-based product for the Mowry formation (Figure 40). Maps such as these will guide a petrophysicist to tell them where their Rw parameter in Archie’s equation must be adjusted [14].

General theory states that an increase in resistivity against a baseline, is a direct indication of hydrocarbon presence. A decrease in resistivity is an indication of water-filled pore volume. Archie’s theory states that an interpreted baseline should be established to interpret the 100% water-filled pore volume zone. From there, based on the above equation, total water saturation in the formation can be determined. The interpretation depends on knowing the resistivity of a 100% water saturated zone (Rw), total porosity (phi), the cementation exponent (m), true resistivity of the formation (Rt), and n (saturation exponent). The tortuosity exponent is often typically included (a). The formation true resistivity can be complicated by an overprint from lithology such as higher resistivity due to tight carbonates or low resistivity, low contrast pay (Figure 41).

Water Saturation raised to the Lithological Factor (Sw^n) = Resistivity of water/(Total Porosity^Cementation Exponent × True Resistivity (from measured log) [14]

Archie’s equation is based on measured principles from sandstones in the Gulf of Mexico and resulting characteristic responses that allow a petrophysicist to calibrate and utilize the known equation [14]. In unconventionals, or silty-sands or shaley-silts etc., excess conductivity related to the cation exchange capacity of clays impacts the basic principles of Archie’s equation. The impact of the total water saturation calculation in a clay-rich zone can be observed.

In this study, total water saturation was evaluated several various ways to ensure the client has the most representative SWT (%) for the formation of interest they are trying to evaluate hydrocarbon-prone potential in.

A conventional Archie total water saturation approach was executed [14] as well as shaley-sand models interpreted across the stratigraphic column. For all the below applied equations and evaluations the total water saturation parameters were held constant and did not vary by stratigraphic zone in every well with depth. Calibration for total water saturation interpretation was utilized from core and rotary sidewall core-based measurements. In addition, to those measurements, Pickett plots were utilized to cross-check and constrain the Rw, M (cementation factor), N (lithology factor) and A (tortuosity) (Figures 42(a)-(c)). Last, to ensure calibration, core based Rw measurements were utilized. Archie based application; Interpreted three ways to represent a low side, P50 and high side solution (Figure 43 and Figure 44).

Archie_SWT_Higher Towards 0% (Increasing So) – Rw = 0.04, MN = 1.7, A = 1

Archie_SWT_P50 – Rw = 0.07, MN = 1.8, A = 1

Archie_SWT_Lower Towards 100% (Decreasing So) – RW = 0.11, MN = 2, A = 1

In addition to the Archie based solutions, shaley-sand models were applied for both a total water saturation solution and as effective water saturation solution. These equations consider the cation exchange capacity (CEC) responsible for resulting in excess conductivity and therefore low resistivity associated with the presence of certain clay volumes (often species dependent). As such, for the low resistivity, low contrast potential pay the shaley-sand solutions were applied with the following parameters:

· Simandoux Rw = 0.07, MN 1.8, Res Clay 2, A = 1

· Indonesian Rw = 0.07, MN 1.8, Res Clay 2, A = 1

These equations were kept constant and applied across the entire stratigraphic stack. Fine-tuning of adjusting these parameters per formation was not conducted as the solutions are meant to provide the client with a high-level look and quantification (Figure 43). Last, the best fit solution for total water saturation interpretation provided to the client is derived from the multi-mineral model and does vary Rw, MN and A across the stratigraphic column as needed (Figure 44).

An extension of our understanding of the partitioning of waters beyond the scope of just a total water saturation solution involves the utilization of nuclear magnetic resonance acquired data. Correlation between nuclear magnetic resonance bound water fraction, as defined by applying a 10 ms cut-off threshold on the open hole wireline data where the tool was run in a 0.2 TE and 2 MHz acquisition setting is applied in this case study (Figure 45). The resulting bound water fraction can be correlated to the volume of clay and an empirical relationship defined. Then that correlation can be extrapolated across the regional setting to provide petrophysical interpretation that allows the investigator to map So (1-SWT), BFV (bound water fraction) and by taking the difference in summation adding to 1 of these fluid fractions, the remaining pore-filled volume is free water (Figure 46(a) and Figure 46(b)). Maps can be generated of each of these fluid fractions. Open retort laboratory data will aid in the constraint and therefore correlation of the bound versus free water interpretation.

One of the most critical roles of a petrophysicist is to constrain and provide an original oil-in place quantification. All the work all leads up to that understanding and volumetric assessment. Previously we presented one way of calculating a continuous wireline based original oil in-place (OOIP) just from the geochemistry data alone. The more common way to calculate and assess the OOIP is with the integration of pressure-volume-temperature (PVT) data as well.

Formation volume factor (FVF or Bo) in the basin ranges from ~1 - 1.5 based on various PVT measurements across the stratigraphic column. A petrophysicist can assume an equation of state model to be applied and with the fluid gravity specifics reported in a PVT report as well as the resulting Bo, interpret a continuous wireline-based Bo. That variable Bo with depth can then be used as the input to the denominator in the OOIP equation. Else in the lack of the ability to generate a continuous curve, a constant value for Bo may be assumed and utilized (Figure 47).

Original Oil-In Place (OOIP) = 7758 × Total Porosity × (1-Total Water Saturation) × Area × height (thickness in feet)/Formation Volume factor (Bo)

For this study and the regionally calculated OOIP, a Bo of 1 was assumed for final calculations. However, below is an example of a PVT constraint in the Mowry formation where the petrophysics was constrained and a continuous interpretation could be generated. Last, below is a petrophysical interpretation of OOIP (assumes 640 acres as the area of investigation and 0.5 for the height of the given calculation) (Figure 48).

For unconventional opportunities, the importance of the rock’s stress and strain behavior is critical to understand. In this study we took the approach to evaluate dynamic Poisson’s Ratio and Young’s Modulus as derived from wireline log application. The dynamic to static relationship was not established so no vertical versus horizontal isotropic or anisotropic models were developed.

Utilizing the bulk density (g/cc) and compressional and shear velocities, dynamic geomechanical properties can be interpreted. In other words, the stress and strain tendency of a given rock volume. Or Poisson’s Ratio (PR and Young’s Modulus (YM psi) [15]. These properties relate to the stiffness and elasticity (or ability to rebound) and are commonly used to understand how parameters for a stimulated completion may force the rock to behave [16 , 17]. Where raw logs of DTC (compressional) and DTS (shear) were not available, the established pseudo-DTC and DTS were used as inputs to derive additional PR and YM coverage (Figure 47).

Assume the following principles:

V = Poisson’s Ratio = Strain in each direction perpendicular to the axial load/Strain in the direct direction of the axial load

Compressional and shear velocities can be interpreted for a VpVs ratio as well as sonic-based porosities on either a sandstone, limestone or dolostone matrices. Also, from these calculations and raw log trends, top overpressure or formational overpressure can be qualitatively and quantitatively interpreted [18 , 19]. Along with the qualitative approach to the observation and definition of overpressure (Figure 49), a more quantitative approach can also be executed. For this study a few select wells were chosen to evaluate pore pressure derived from resistivity and acoustic logs along the stratigraphic column. Limited DFIT data was available for calibration so the evaluation should be used with caution. Further work to constrain via leak-off test (LOT), formation integrity test (FIT) and DFIT data would be advised and welcome. In general, a pore pressure versus depth trend can be established and written in the linear relationship form to then derive on all wells in each basin. While that straight-forward solution does not consider the basin history or complexity, it can qualitatively aid in evaluating the top overpressure window spatially.

For each well investigated a workflow series of calculations was performed.

Step 1: Density Estimation via Gardner, Bellotti and Lindseth methods was applied with constants inputs as follows: Gardner “a” = 0.23 and “b” = 0.25, Bellotti consolidated formation Rho = 3.28-DT/89 and where Rho = (Vp-3460)/(0.308XVp). Where density estimation did not have to be performed and raw values could be utilized this was preferred as the input to Steps 2 and 3.

Step 2: Overburden Gradient Calculation where the KB height must be known, Water depth must be known, and density must be known. For this study, a density of water of 1 g/cc was utilized. Intervals where density was missing, or estimation was not performed an Amoco compaction relationship was assumed.

Step 3: Overburden, Fracture Gradient and Pore Pressure Calculations Required Inputs and Constraints

Volume of Clay, KB, Water Depth, Water Density, Resistivity or Sonic curve, applied method-For this study we applied a modified Eaton’s method where the Eaton constant was held at 1.2. We note that measured Biot’s coefficient in this basin does range from 0.8 to 1.1 and should be used to tune pore pressure calculations appropriately.

The resulting analysis provides a continuous fracture gradient and pore pressure predicted curve across the stratigraphic column for a given well. A geologist can then use this resistivity and acoustic derived deliverable to map and supplement any DFIT or point analysis they may have in each basin (Figure 50).