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.
When a piece of rock at depth is cut out to be a core sample by drilling, it becomes free from rock stresses and expand in radial direction. The expansion occurs in an asymmetric manner with the relief of anisotropic in-situ stresses, and it results in a sinusoidal variation of core diameter with a period of 180 deg. in the circumferential direction. The circumferential variation of core diameter is given theoretically as a function of in-situ stress. These new findings can lead various ideas to determine the in-situ stress from circumferential variation of core diameter measured after the core retrieving. In the most simple case when a single core is only available, the difference between the maximum and minimum components of in-situ stress in a plane perpendicular to the drilled hole can be estimated from the maximum and minimum core diameters. If several cores with different orientation are available, all of three principal components of three dimensional in-situ stress can be determined. The technique of side-wall coring is one of possible ways to take core samples with different orientations even after a borehole has been drilled. The theoretical relationship between the core expansion and rock stress has been verified through the examination of the core prepared in laboratory experiments and retrieved field cores.
Geomechanical approaches applied to oil and gas exploitation at deep depths are becoming common place. Shale gas development is a typical example for which such approach is inevitable. Rock stress is a principal factor dominating the geomechanical behaviors of rock. We have developed recently a method using core samples, which is referred to as Diametrical Core Deformation Analysis (DCDA) (Funato and Ito, 2017). Core samples expand elastically in an asymmetric manner by being relieved from anisotropic in-situ stresses, and the magnitude and orientation of in-situ stress can be estimated from the circumferential variation of the measured core diameters.
Core permeability modeling as a function of well logging data is a crucial issue to obtain accurate prediction for the same well or other wells, which have lack of core measurements. The distinct scale measurement of well logging of few feet and core of few inches is one source of that prediction problem. Additionally, the well logging data might have joint effect on the core porosity measurements, which is usually neglected for its correlation in the core permeability modeling. In this paper, an integrated cross-variogram approach was developed and applied to model the core permeability as a primary factor along with core porosity as a secondary factor given the well logging data. The available well logging interpretations for the available oil well include neutron porosity and water saturation as well as depth. The cross-variogram was developed for the co-kriging interpolation to provide linear Coregionalization modeling of core permeability and porosity. Since an auxiliary variable measured at the same points as the target, its effect, besides the target value of interest is cross-correlated to be used when predicting the target values at unsampled intervals. The co-kriging was adopted in comparison with the ordinary kriging. The accuracy of permeability prediction were quantified with respect to training/testing cross-validation of dataset as well as the mismatch between predicted and measured core permeability through the co-kriging and ordinary kriging algorithms. Results showed that co-kriging has much more accurate permeability modeling and reasonable prediction than the ordinary kriging by attaining the least root mean square error and highest adjusted R-square. That workflow was applied on a well in sandstone reservoir from Zubair formation in South Rumaila oil field, Located in Iraq.
Modeling and estimating core permeability is a pivotal step in reservoir characterization as it interrelated to data sparseness along with distinct scales and sources. Therefore, it is essential to look for efficient modelling approaches to model core permeability of few inches scale as a function of well logging data of few feet scales.
Drilling a well safely is the cornerstone of our industry. Fields with complex geology add another set of challenges due to the higher uncertainty on formation dips, fractured or weak formation to name a few. The most unstable zones in the well construction process are thinly bedded shales. Their laminated nature creates variations in rock strength between the different layers making them very susceptible to rock failure. Unfortunately, these formations are very common and can occur at any stage of the well construction.
Traditionally, a one dimensional (1D) geomechanical pre-drill model is prepared using offset data (conventional triple combo with sonic data) and is used for mud weight calibration and to prepare the appropriate action plan to be implemented during the drilling of the well. While drilling, real-time triple combo, and acoustic data are acquired, refining the 1D geomechanical model, reducing the uncertainty with each newly acquired data point, enabling safer drilling.
The new workflow adds another crucial component: Rock strength characterization. Rock strength comprises the Unconfined Compressive Strength, the Tensile Strength, and the Friction Angle. These factors are critical to estimating the shear failure during wellbore stability analysis. Rock strength is obtained from rock laboratory tests, but when unavailable, it can be modeled and further refined while drilling from sonic measurements and porosity if the angle between the wellbore and the formation is known while drilling.
The pre-drill model is built using dip information from surface seismic data. During the drilling process, dip information needs to be gathered throughout the overburden regardless of lithology. Using a deep directional resistivity tool commonly used in well placement, the dip of the formation is computed continuously in the shales and the sands. The 1D geomechanical model is now refined in real-time with the triple combo, multipole acoustic, and dip data. The additional data acquired in real-time drastically reduces the uncertainty, not only, of the mud weight calibration, but also the newly added component of rock strength.
Kaneko, Fuyuki (Tohoku University) | Nakamura, Kengo (Tohoku University) | Sakamoto, Yasuhide (National Institute of Advanced Industrial Science and Technology (AIST)) | Watanabe, Noriaki (Tohoku University) | Komai, Takeshi (Tohoku University)
Natural gas production from methane hydrate reservoir requires supplying heat. However, it is necessary to decrease energy consumption, and some new ways for enhanced gas production are investigated. One of them is partial-oxidation and heating process, which is proposed newly by us. This process is injecting acid or oxidant into the reservoir, which makes several effects for enhanced gas production. One of them is heat effect which comes from mineral dissolution by acid injection. In this study, we evaluated this with simulation.
In order to carry out this calculation, we extended conventional numerical simulation model, and some parameters were needed. These parameters were about the dissolution rate and the heat. Therefore, we made the flow experiment with a column and measured the heat of mineral dissolution with a differential scanning calorimetry. In this experiment, Toyoura sand as a sediment and hydrochloric acid were used. In based on those experimental results, we determined constants.
We carried out three types of numerical analyses about injecting acid into the reservoir. The first of them was the calculation about the reservoir which has no methane hydrate. The next was about the methane hydrate reservoir. The last was also about the methane hydrate reservoir, but injected acid temperatures were changed. In these calculations about the methane hydrate reservoir, the initial saturation of methane hydrate is 20 % on the assumption that this process as the secondary recovery follows the depressurization.
The results show that injecting acid whose concentration is 0.50mol/L heats the reservoir by approximately 30℃ from the initial temperature of methane hydrate reservoir. Hence, the total gas production in that case is comparable with that in hot water injection at 50℃. Additionally, the lower the temperature of injected acid is, the higher the effect of reaction heat of mineral dissolution is. In conclusion, acid injection to methane hydrate reservoir will be the effective way of enhanced natural gas production.