This first tutorial is aimed at those analysts of digital well logs who are lulled by slick pictures of computed answer products into believing the task is easy and quickly done. One is led to believe that all one needs to do is to buy a seat on a fancy digital well-log application, take a short course in what all the buttons do, then crank out porosity, water saturation and net-pay values that are correct and verifiable. (Pardon me while I chuckle!) One of my mentors (Lou McPherson) often repeated the phrase, “all logs are damned lies,” and of course he is correct. No wireline log recorded at the wellsite actually reads the true reservoir property printed on the well-log heading (or file name of a digitally recorded well log). So, I hope to impress upon the reader that there is much to be done before using recorded data “as is” and much to be understood about the algorithms and their limitations in the fancy black box. It is far from being an “easy” task and manifestly is never a “quick” one. The well logs are but one of at least five data streams that one must analyze and synergize with well logs to hope to approach an acceptable result for a reservoir property within a 12-in. radius around the borehole; those other data streams that must be analyzed and simultaneously considered with wireline well logs are (1) mud logs, (2) cores and core analyses (looking at the rocks is so very important), (3) possibly logging-while-drilling (LWD) logs, (4) wireline formation test (WFT) data, and perhaps (5) drillstem test (DST) observations. Not all wells will have a full complement of data streams, but the missing data must be considered when placing error limits on any calculated values and predictions. In addition to the aforementioned difficulties, and before using a digital well-log processing (DWLP) program (commercial or proprietary), one needs to consider some often overlooked yet critical “points or steps or truisms.”
Lack of accurate estimation of reservoir permeability has been one of the most challenging problems for enhanced hydrocarbon recovery. Pore-structure variation in carbonate rocks caused by diagenesis controls reservoir permeability heterogeneity. In this paper, we propose a rock-physics-based method of quantitative characterization of pore structure and permeability heterogeneity using core and sonic logs. Mercury injection capillary pressure (MICP) and Leverett J-function curves can be first used to classify the pore systems and permeability variation in the reservoirs. Shear frame flexibility factor (γµ) derived from sonic logs is further used to quantify the pore type and permeability variation in the reservoir zones. In the studied Puguang gas field, different pore systems in five reservoir zones are identified: moldic, sucrosic macrointercrystalline, mixed moldic and intercrystalline, meso-intercrystalline, and micro-intercrystalline pores respectively in the upward shallowing sequence of the Early Triassic reservoir. Permeability varies significantly between the five zones. Results show that at a fixed porosity, moldic pores show higher velocity, resistivity, yet much lower permeability than intercrystalline pores. When γµ < 4, the reservoir zone is dominated by moldic pores; when 4 < γµ < 8, meso- to macro-intercrystalline pores are dominant; and when γµ > 8, micro-intercrystalline pores are prevalent. Two different permeability-porosity trends controlled by distinctive pore types are also distinguishable by γµ. Reservoirs dominated by isolated moldic pores, at a given porosity, has much lower permeability than the ones dominated by connected intercrystalline pores. The results on pore-type discrimination and permeability estimation have been successfully used to understand the production problems in the Puguang reservoir.
In our business, accurate subsurface measurements are crucial. Depth is the most fundamental measurement made, tying together all the various along-hole measurements made and services provided. Logging-while-drilling (LWD) depths are based on driller’s depths. Driller’s depths have been plagued with accuracy issues, with numerous papers highlighting this.
This paper combines a wireline-depth-determination methodology with driller’s depth and shows how this can be used to arrive at a calibrated and corrected drillstring depth. The objective is to arrive at “true along-hole (TAH)” depth. Driller’s depth measurement is based on drillstring length, typically as identified in the “tally book”. Together with the dimensions of the bottomhole assembly (BHA), the measured pipe lengths represent the calibrated drillstring length.
Similar to wireline correction, a way-point method is described for correcting the drillstring length for thermal elongation and elastic stretch. This allows a corrected depth to be defined for the bit and the associated LWD sensors. A crucial difference to conventional driller’s depth is that the way-point method is applied during pull out of the hole (POOH). Most of the parameters that cause complications in driller’s depth correction are mitigated when pulling out of hole. The correction elements of thermal elongation and elastic stretch are the only ones then applicable. The waypoint method described can provide corrections in wells with complex and extended-reach trajectories.
The paper discusses how to arrive at an uncertainty so that measured depths are the TAH depth and that these TAH depths can be verified against repeat runs and wireline measured depth.
Along-hole depth is the basis of all well construction, reservoir and field modeling and economic evaluations. Depth impacts all aspects of the oil and gas upstream subsurface activity. Driller’s depth is derived from a composite record of the drilling activity as the drillstring moves into the well during the drilling process. The economic need for absolute and relative depth accuracy is not discussed in this paper, but it is clear that there are efficiency gains and asset value improvements with increased certainty and congruence of depth data.
Reliable estimation of kerogen density is a requirement for dependable well-log-based petrophysical evaluation of organic-rich mudrocks. As kerogen matures, hydrocarbons are generated and the chemical structure of kerogen is transformed, which can lead to measurable variations in kerogen density. Uncertainty in estimates of kerogen density can significantly impact the reliability of well-log interpretation results.
The objectives of this research are (a) to experimentally quantify the density of kerogen isolated from a variety of organic-rich mudrocks with different origins, (b) to investigate the impact of thermal maturity on kerogen density, and (c) to investigate the impact of synthetic maturation on density of kerogen. We used organic-rich mudrock samples from four formations, to cover a wide range in kerogen thermal maturity. We isolated kerogen from these mudrock samples and estimated the density of the naturally and synthetically matured isolated kerogen samples.
The experimental results indicated that the density of kerogen varies significantly among organic-rich mudrocks with different origins. We recorded densities ranging from 1.19 to 1.77 g/cm3 in kerogen samples when the hydrogen index varied from 603 to 48 mg hydrocarbon/g organic carbon. We also observed that kerogen density increases as a function of thermal maturity. Sensitivity analysis confirmed a measurable impact of kerogen density on estimates of petrophysical properties, such as porosity and water saturation in organic-rich mudrocks. The documented experimental results and procedures can be used to enhance petrophysical evaluation of organic-rich mudrocks, by taking into account the impact of kerogen thermal maturity in the models used for interpretation of core or well-log measurements.
Kerogen disseminated in organic-rich mudrocks presents challenges when performing well-log-based petrophysical evaluation. One such challenge is the lack of reliable estimates of kerogen density in organic-rich mudrocks. Inaccuracies in estimates of kerogen density can negatively influence assessments of porosity, mineralogy, and water saturation in organic-rich mudrocks.
The Gas Research Institute (GRI) conducted pioneering work on measuring shale petrophysical properties in the 1990s, however, despite growing interest in shales, there are still no set standards with respect to obtaining core petrophysical measurements due to the inherent complexity of shales. Core cleaning is one aspect of this problem.
The objective of this study is to shed some light on the shale core-cleaning conundrum. The study shows the cleaning impact of different solvents on samples from different maturity windows and having different in-situ fluids. It also compares the cleaning efficiency between plug and powdered samples. Different cleaning apparatus, such as the high-pressure extractor (HPE) and the Soxhlet extractor, are also compared.
Different measurements, such as source-rock analysis (S1 and S2 values); gas chromatography-mass spectrometry (GC-MS) extraction analysis; Brunauer-Emmett-Teller (BET) surface area and pore-size distribution help to understand the dynamics of core cleaning. This study was carried out on samples from the Wolfcamp and Eagle Ford formations.
Cleaning has a major impact on various petrophysical properties like porosity (increases up to 50%), S1 (decreases up to 90%) and surface area (increases by 450%). This study showed that cleaning time is a function of maturity and sample state. Samples in the oil-maturity window are much more difficult to clean compared to the samples in the gas-maturity window. Similarly, plug samples are more difficult to clean compared to the crushed samples. Our study shows that toluene, dichloromethane (DCM) and chloroform have similar cleaning efficiencies but n-heptane is less efficient.
Coring is an integral part of any exploration program. The planning for a coring program, coring fluids and corehandling procedures at the wellsite are all very important for preserving the core and getting accurate measurements in the laboratory.
Three-dimensional (3D) printing is a unique technology that enables building of 3D pore-network proxies from digital models. Proxies allow us to experimentally test petrophysical properties (e.g., porosity and permeability) that can supplement reservoir rock analysis. In this study, we tested the resolution and accuracy of a polyjet 3D printer for generating rock proxies from a digital model of Berea sandstone. A 20×25-mm (length × diameter) cylindrical sandstone “macroplug” (21.6% porosity) and a smaller 3.5×4.0-mm “microplug” (21.3% porosity) were analyzed with mercury intrusion porosimetry and were scanned with computed tomography at 10 and 4 µm per voxel, respectively. A microplug digital model, with a porosity of 21.3%, a volume of 8 mm3, and a modal pore-throat diameter of 18 µm, was extracted from tomographic data and rescaled at 10x magnification to meet the minimum pore resolution of the 3D printer (~132 µm). Proxies and core-plug samples were compared for porosity and pore-throat size distribution using two approaches: (1) mercury porosimetry; and (2) digital measurements from tomographic data. This comparison revealed a decrease in proxy porosity by ~2 percentage points and a decrease in pore-throat diameter by ~56 µm relative to natural samples. These discrepancies could arise due to insufficient magnification of the digital model or due to incomplete removal of the wax support material from the proxy pore space. Development of enhanced cleaning methods for pore space in polyjet proxies is needed to generate more accurate reservoir rock models.
Multiscale reservoir characterization requires an understanding of a rock’s mineralogical and textural characteristics as well as physical and chemical properties of fluids occupying its pore space. Pore geometry and topology also control key petrophysical properties such as porosity and permeability (Doyen, 1988; Bera et al., 2011; Peng et al., 2012). These properties determine reservoir quality with respect to the extraction of water and hydrocarbons, or the sequestration of carbon dioxide.
Shetty, Sushil (Schlumberger-Doll Research Center) | Liang, Lin (Schlumberger-Doll Research Center) | Simoes, Vanessa (Schlumberger Brazil Research and Geoengineering Center) | Canesin, Fabio (Schlumberger Brazil Research and Geoengineering Center) | Boyd, Austin (Schlumberger Brazil Research and Geoengineering Center) | Zeroug, Smaine (Schlumberger-Doll Research Center) | Sinha, Bikash (Schlumberger-Doll Research Center) | Habashy, Tarek (Schlumberger-Doll Research Center) | Domingues, Ana Beatriz (Shell Research PTI - Brazil R&D Team) | Amorim, Claudia (Shell Research PTI - Brazil R&D Team) | Abbots, Frances (Shell Research PTI - Brazil R&D Team)
We present an automatic inversion for petrophysical interpretation of data acquired in a vertical well by the array sonic, array induction, and density logging tools. The inversion is especially suited for challenging interpretation scenarios where the formation is drilled with oil-based mud and has complex mineralogy consisting of multiple minerals and pore types. The inversion accounts for the simultaneous effects on all the tool sensors from filtrate invasion, gas phase, complex mineralogy and mechanical damage. The resulting interpretation is robust, accurate, and honors the multiple radial investigation depths of the different tools.
The inversion estimates formation porosity and radial distributions of pore shape, oil, gas, and water saturation extending several feet from the wellbore. Radial changes in fluid saturation and pore shape are caused by filtrate invasion and mechanical damage respectively. Sonic and electromagnetic forward solvers are used to simulate data for different tools. The solvers are linked to formation properties through a saturation transform and an effective-medium rock physics model. Properties of the formation are estimated by the inversion such that the simulated data match the measured data at each log depth. For the first time, sonic data for both dipole flexural-wave and monopole compressional-headwave are included in the inversion. These data are sensitive to porosity and pore-shape effects, and the compressional-headwave additionally provides sensitivity to gas saturation in soft formations.
We tested the inversion on synthetic data and two field datasets for a gas-bearing formation drilled with oil-based mud. The results are visualized as 2D images with radial distribution of properties at each log depth. The images characterize depth of filtrate invasion and mechanic damage for guiding completion and production decisions. The images also provide far-field fluid saturation and porosity for reserves calculations. The far-field properties are in overall good agreement with core data and traditional interpretation, with differences from traditional interpretation of saturation and porosity in key intervals. Quality controls are included to check the validity of approximations underlying the inversion. The results demonstrate an efficient inversion framework for guiding reserves, production, and completions decisions in challenging scenarios.
Blount, Aidan (Shell Exploration and Production Company) | Croft, Tyler (Shell Exploration and Production Company) | Driskill, Brian (Shell Exploration and Production Company) | Tepper, Brian (Shell Exploration and Production Company)
In today’s competitive cost environment, core acquisition and analysis is too often dismissed as unaffordable. This forces petrophysicists to make every dollar count in core evaluation. Tough choices have to be made—many people chase the lowest bid, least expensive methodologies, reduced oversight, and less sampling. In this paper, insights will be shared from a comprehensive round-robin study directly comparing the results of the most common techniques (GRI/Retort/RCA) used by major vendors. Understanding differences in techniques early in an evaluation process can help efficiently direct technical spending.
As with many comparison studies, this project started with the reconciliation of analysis sourced from different laboratories using different methodologies.
There was a significant business driver to this work as we noticed differences in measured porosity and fluid saturations that contribute to significant differences, approximately 25%, in hydrocarbon pore volume among vendors using alternative techniques. These differences directly impact log calibration objectives as well as estimations of hydrocarbons in place.
We began to ask a series of simple questions: Should we use crushed samples or routine core plugs? What is the impact of analytical technique on the results? What role does lithology and organic content play in the results from different analytical techniques? What is the role of sample size? What is the variability between vendors for identical procedures? If there is variability, what is the apparent cause?
A set of 10 twin samples of Permian Bone Spring formation from the Delaware Basin in Texas and New Mexico was evaluated using a variety of laboratory-derived measurements, including X-ray diffraction (XRD), total organic carbon (TOC)/RockEval, retort, and Dean- Stark/Gas Research Institute (GRI) protocol analyses from two labs and RCA from one lab. These 10 samples were selected to represent varying lithofacies with a range of organic, mineralogical, and water/oil content. The level of oversight at each data source was also tracked.
Through detailed analysis of the raw data from these measurements, we address the questions above. With these results, we hope to (1) maximize every dollar spent in core analysis, (2) focus oversight where it is truly required, and (3) accurately and consistently evaluate the core analysis in the Permian play for fast and value-driven business decisions.
This paper narrows down the knowledge gap in interpretation of electrical resistivity measurements in oil-wet and mixed-wet formations by analytically deriving a new resistivity model that can reliably estimate hydrocarbon reserves at different levels of wettability. The objectives of this paper include (a) to quantify the influence of wettability on electrical resistivity measurement, (b) to develop a new analytical resistivity model that takes into account the impacts of wettability on electrical resistivity, and (c) to improve the assessment of hydrocarbon saturation by introducing a wettability-dependent parameter into a new resistivity method. The new resistivity model not only incorporates wettability of the rock, but also a directionally conducting fractional pore network to honor rock fabric.
The aforementioned features are quantitatively evaluated from the three-dimensional (3D) pore-scale images, taken from each rock type in the formation. We apply a semianalytical streamline numerical model to estimate pore-network connectivity in the 3D binary images. The resistivity and the calculated geometry-related parameters are used as inputs to the new model in order to estimate water saturation. To test the performance of the introduced method at different levels of wettability and water saturation, we synthetically saturate the porescale images with water and oil at different wettability configurations and water saturation, honoring the physics of intermolecular interactions between different fluid and solid components.
The results obtained from the new method are compared against the actual saturation. We successfully applied the introduced method to carbonate rock samples with wettability ranging from strongly oil-wet to strongly water-wet. The electrical resistivity results obtained from numerical simulations were in agreement with the resistivity estimates from the new method. The results also showed that wettability has a significant influence on electrical resistivity of the rocks at water saturation levels below 50%. Moreover, we demonstrated that the proposed model provides reliable results when applied to field data. The outcomes of this paper are promising for well-log-based applications of the new method in complex mixed-wet formations.
The natural gamma-ray API formation maintained by the University of Houston (“UH formation”) defines the API unit to which natural gamma-ray tools are calibrated. Unfortunately, the narrow borehole of the UH formation cannot accommodate logging-while-drilling (LWD) tools, and planned expansion of the university will soon make the formation unavailable. This paper lays the groundwork for replacing the UH formation with a combination of computer modeling and a single calibration point. The effectiveness of the method is illustrated with a wireline tool and an LWD tool.
This method defines a formation to be used with computer models (“digital API formation”) that emulates the UH formation. However, unlike the UH formation, the digital API formation has an uncased borehole. With modeling, it is easy to vary the size of the borehole to match the tool size being calibrated. To account for imperfections in the tool model, the model is calibrated by comparing its predictions to physical-tool measurements in a large tank of potassium chloride brine. Tool sensitivity is computed by dividing the calibrated count rate computed for the digital API formation by the API value assigned to the formation.
Designing the digital API formation began by developing a computer model that emulates the UH formation. The count rates computed with this model for a wireline tool matched the corresponding measured count rate to within 1%. Like the UH formation, the source of the digital API formation contains a combination of potassium, thorium, and uranium. The relative combination of these elements was determined so that the spectrum of photons on the surface of a wireline detector in the digital API formation is the same as in the UH formation. The absolute concentrations were defined so that a wireline tool would have the same count rate in both formations.
This method is shown to match the sensitivity of a wireline tool that was calibrated in the UH formation to within 1%. The method was also used to compute the sensitivity of an LWD tool, which compares favorably to the measured sensitivity determined with granite blocks. Using this method, the UH formation can be safely discarded. In addition, better agreement between wireline and LWD logs can be obtained because they are all calibrated in the same formation and in their natural logging positions. Details of the digital API formation are disclosed.