Tag de Notion
Type de Fichier
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.
This paper presents a continuation of our fundamental investigations into the 2D T1-T2 NMR response of saturating fluids in the organic-matter pores of pelletized kerogen isolates. We previously reported that T1-T2 data of heptane-saturated kerogen pellets showed two distinct peaks: (1) a slow-relaxing peak, interpreted as heptane in the kerogen intergranular pores created during pelletization, and (2) a fast-relaxing peak with large T1/T2 ratio, interpreted as heptane absorbed in kerogen granules, i.e. in intragranular pores.
In this study, we investigate the influence of bitumen extraction on the T1-T2 data of the heptane-saturated kerogen pellets, and we use supporting data, such as kerogen swelling effects, nitrogen adsorption BET, and UVVIS absorption spectroscopy to enhance the interpretation of the NMR data. We find that for the fast-relaxing peak, the T1 and T2 values remain roughly the same after bitumen extraction, however the porosity decreases, which strongly suggests that the fast-relaxing peak is associated with heptane absorbed in the intragranular pores of bitumen and kerogen. For the slow-relaxing (intergranular) peak, we find that the porosity remains roughly the same after bitumen extraction, however the T1 and T2 values increase due to a decrease in apparent surface relaxivity, which we attribute to (weak) diffusive-coupling effects between the inter and intragranular porosities.
Our findings provide key insight into the role of kerogen and bitumen on the NMR response in organic shale, which can be used to improve fluid typing and saturation estimates from 2D T1-T2 NMR data, both in the lab and from downhole logs.
In the past decade, as the oil and gas production from unconventional reservoirs increased dramatically, the investigations into organic shale have greatly stimulated both NMR log data interpretation and NMR core analysis (Jiang et al., 2013; Kausik et al., 2016; Reeder et al., 2016; Anand el al., 2017; Tandon et al., 2017; Washburn and Cheng, 2017). Among them, studies focused on kerogen have become more and more popular (Ertas et al., 2006; Chen et al., 2012; Singer et al., 2016, 2017; Zhang and Daigle, 2017). Kerogen, which is defined as solid, insoluble and immobile organic matter, constitutes most of the total organic content (TOC) of organic shale (Durand, 1980), which makes characterizing kerogen essential for formation evaluation.
Zuo, Julian (Schlumberger) | Gisolf, Adriaan (Schlumberger) | Pfeiffer, Thomas (Schlumberger) | Achourov, Vladislav (Schlumberger) | Chen, Li (Schlumberger) | Mullins, Oliver C. (Schlumberger) | Edmundson, Simon (Schlumberger) | Partouche, Ashers (Schlumberger)
Formation fluid properties are critical inputs for field development planning. Acquisition of representative, low-contamination, formation fluid samples is key to obtaining accurate fluid properties from laboratory analysis. Quantification of oil-based-mud (OBM) or water-based-mud (WBM) filtrate contamination of hydrocarbon or water samples is still one of the biggest challenges, both during real-time formation-tester sampling operations and with surface laboratory techniques. Laboratory sample analysis uses either the skimming or the subtraction method to quantify OBM filtrate contamination of hydrocarbon samples whereas tracers are typically required to quantify WBM filtrate contamination of water samples. Recently, a new real-time workflow has been developed to quantify OBM or WBM filtrate contamination of hydrocarbon or water samples with downhole multisensor formation-tester measurements. When discrepancies are observed between laboratory-derived and real-time contamination quantification, it can be challenging to uncover the source of the difference or to identify the most accurate method. This paper evaluates the applicability of different methods.
Surface laboratory methods to quantify OBM filtrate contamination crucially assume that the mole fraction of components in the C8+ portion of uncontaminated reservoir fluids and the corresponding molecular weights (or carbon numbers) follow an exponential relation. When actual fluid compositions deviate from the assumed behavior, a large error in OBM filtrate contamination quantification can occur. In this paper, more than 20 laboratory-created mixtures of formation fluid and mud filtrate are analyzed to validate the laboratory methods. Errors of 2 to 3 wt% in OBM filtrate contamination quantification were observed for virgin reservoir fluids that follow the assumed behavior. However, much larger errors may be observed for biodegraded oil, oils with multiple charges from different sources, or oil with similarly wide ranges of compositions to OBM filtrate.
A new workflow allows quantification of OBM or WBM contamination using multiple downhole sensors, for real-time measurement, with unfocused and focused sampling tools for water, oil, and gas condensate. The new workflow comprises five steps: (1) data preprocessing; (2) endpoint determination for a pure native formation fluid using flow-regime identification; (3) endpoint determination for pure mud filtrate and quality control of all endpoints using linear relations between measured fluid properties; (4) contamination determination in vol% and wt% on the basis of live fluids and stock-tank liquids; and (5) decontamination of the fluid properties including gas/oil ratio, density, optical density, formation volume factor, resistivity, and compositions.
The new workflow has been applied to a large number of field cases, with very good results. For most of the cases, the downhole analysis is consistent with the surface laboratory results.
When discrepancies between methods are observed, a thorough understanding of the limitations of each technique, as described in this paper, will help to determine which data to bring forward and what to discard.
Andersen, Pål Østebø (University of Stavanger and The National IOR Center of Norway) | Skjæveland, Svein Magne (University of Stavanger and The National IOR Center of Norway) | Standnes, Dag Chun (University of Stavanger)
Primary drainage by centrifuge is considered where a core fully saturated with a dense wetting phase is rotated at a given rotational speed and a less dense, nonwetting phase enters. The displacement is hindered by a positive drainage capillary pressure and equilibrium is approached with time. We present general partial differential equations describing the setup and consider a multispeed drainage sequence from one equilibrium state (at a given rotational speed) to the next. By appropriate simplifications we derive that the process is driven by the distance from equilibrium state as described by the capillary pressure at the inner radius and position of the threshold pressure (transition from two to one-phase) from their equilibrium values. Further, an exponential solution is derived analytically to describe the transient production phase. Using representative input saturation functions and system parameters we solve the general equations using commercial software and compare with the predicted exponential solutions. It is seen that the match is excellent and that variations in timescale are well captured. The rate is slightly underestimated at early times and overestimated at late times, which can be related to changes in total mobility during the cycles for the given input.
Measuring capillary pressure using the centrifuge approach (Ruth and Chen, 1995) is one of several methods available for obtaining such curves. Other methods include the use of membranes/porous disks (Lenormand et al., 1996; Hammervold et al., 1998) and mercury injection capillary pressure (MICP) (Purcell, 1949). Each method has benefits and disadvantages regarding time consumption and interpretation of the measured data. Most of the methods consist of exposing a rock sample saturated with a representative fluid composition to various pressure conditions where the equilibrium state corresponds to a unique distribution of fluids and hence capillary pressure in the sample (Forbes, 1997). The fluid saturations corresponding to these equilibrium states are determined by the observed fluid production.
Zhang, Quanying (China University of Petroleum) | Zhang, Feng (China University of Petroleum) | Liu, Juntao (China University of Petroleum) | Wu, He (China University of Petroleum) | Wu, Guoli (China University of Petroleum) | Jia, Wenbao (Nanjing University of Aeronautics and Astronautics) | Ti, Yongzhou (Isotope Research Institute of Henan Academy of Sciences Co. Ltd., Zhengzhou) | Li, Jing (Guta Branch School of Mongolian Senior High School)
Pulsed-neutron gamma density (NGD) logging, as an emerging density measurement technology is of significance for radioprotection and technological development of logging-while-drilling (LWD). Compared to the gamma-gamma density (GGD), the NGD technique has the advantages of environment, safety and health. However, due to the lack of theory, the quantitative relationship between the inelastic gamma field distribution and formation parameters has not been resolved so far. The current data-processing methods are mainly based on the empirical formulas obtained by experiment and simulation methods.
In order to quantitatively clarify the logging mechanism and theoretically develop a new density algorithm, the coupled idea is introduced to NGD logging. Based on the theories of fast-neutron scattering and gamma attenuation, the fast neutron-gamma coupled field theory is put forward to describe the distribution of inelastic gamma field. The inelastic gamma field distribution is characterized by the inelastic scattering cross section, fast-neutron scattering free path and formation density, and the influence of the formation parameters on the field distribution is quantified and a new density algorithm is derived from the coupled theory. The new density algorithm can avoid the complex correction of hydrogen index and simplify the process of density measurement. In addition, the coupled field theory and the new density algorithm are verified by Monte Carlo simulation. The research not only clarifies the NGD mechanism but also provides theoretical guidance for NGD logging.
Formation density is one of the most important parameter for formation evaluation, particularly in oil and gas exploration. The traditional gamma-gamma density (GGD) employing a Cs-137 radioisotope as a gamma source has raised certain health, safety, and environmental (HSE) concerns. The companies working with radioisotope sources have to follow rigorous standards and suffer enormous cost on the packaging, storage, transportation, handling, and disposal of the materials (Badruzzaman et al., 2004; Alakeely and Meridji, 2014).
Downhole formation pressure gradients are often estimated by defining the relationship between formation pressures and depths using ordinary least-squares regression (OLS). These lines are fit based on the implicit assumption of no depth measurement errors when using a pressure-on-depth regression model. For almost all pressure surveys, depth measurement errors are non-negligible and can significantly impact the estimation of the pressure gradient, sometimes to a level of 0.1 psi/ft. Because OLS pressure-on-depth regression models do not account for depth measurement errors, these models produce estimates of pressure gradients that are too low. Conversely, OLS depth-on-pressure regression models (pressure measurement errors and no depth measurement errors) produce pressure gradient estimates that are too high. Both models result in uncertainty estimates that are too narrow. Therefore, a modified OLS regression model (MOLS) was developed to account for errors in both measurement and to make better uncertainty estimates. MOLS is an extension of OLS regression modeling as it incorporates the limits or boundary conditions defined by the OLS regression models and incorporates the method of moments to make a more accurate estimate of the true pressure gradient. The MOLS methodology was validated against orthogonal regression models, which incorporates error in both measurements, via synthetic datasets as the errors in depth and pressure are known. Once MOLS was validated, these same models were used to quantify the impact of measurement error stemming from common pressure-survey design components and operational overprints. The number of pretests acquired and the interval thickness are directly related to the overall certainty of a given pressure gradient. Fewer pretests are needed to achieve the same level of certainty in a thicker interval relative to a thinner interval. Making one or two additional pretests does not have a high probability of reducing uncertainty. The potential of nonrepresentative measurement error, for a specific pressure survey, also has a significant impact on the accuracy and uncertainty of a pressure gradient. If the absolute sum of the measurement error is large, biased estimates of a pressure gradient result.
For actual pressure-survey data, nonrepresentative measurement error is difficult to identify and the acquisition of additional pretests on subsequent logging runs or passes does not reduce this risk. Repeating individual pressure measurements, through multiple drawdowns at specific depths, also does not significantly reduce uncertainty or the probability of nonrepresentative measurement error. Breaking larger intervals into smaller segments potentially introduces larger uncertainties and increases the risk of nonrepresentative measurement error. Additionally, if any of those intervals are associated with depth recalibrations, the probability of inaccurate estimates of the pressure gradient increases. Therefore, segmentation of an interval can increase the potential for false interpretations of vertical flow boundaries driven by an apparent multiple-gradient solution. All of these factors make the differentiation of pressure gradients between sands difficult. Under ideal conditions, the difference in pressure gradients must be bigger than the sum of their standard deviations to be statistically accurate.
The accurate identification of the different components of unconventional tight-oil reservoirs is fundamental to determining reservoir quality. Whereas the existing methodologies for the identification and separation of these components from cores in unconventional shale are both time-consuming and destructive, high-field nuclear magnetic resonance (NMR) is not and may be a viable solution. Low-field NMR downhole logging can assist with a subset of these measurements based on 2D NMR T1-T2 maps. Diffusion measurements play a unique role in the identification of the producible fluid having relaxation times of several tens of milliseconds.
A comparison of 2D NMR T1-T2 measurements on the Upper Bakken source rock with the Middle Bakken and Three Forks intervals at 2-MHz and 400-MHz Larmor frequencies reveals clear differences between the T1/T2 ratios of the different fluids. The bitumen T1/T2 response is sensitive to its environment, varying in value from 20 to 26 in the kerogen-rich Upper Bakken interval to about 2 to 6 for the Middle Bakken and lower Three Forks at 2-MHz. One of the main challenges for low-field NMR T1-T2 relaxometry is the insufficient contrast between the bitumen and clay-bound-water signals. High-field NMR T1-T2 maps at 400-MHz Larmor frequency enable separating the kerogen/bitumen and the clay-associated water by using the frequency dependence of T1. This separation is enabled by the high T1/T2 ratios of the kerogen and the bitumen of more than 1,000 in comparison with that of clay-associated water, which has a relatively lower T1/T2 ratio, on the order of a few hundred at 400-MHz 1H Larmor frequency. Fluids in the inorganic porosity relaxing at a few tens of milliseconds have insufficient T1/T2 contrast to enable their identification. Diffusion measurements can fill this gap in fluid typing by helping in their identification as demonstrated for the Middle Bakken and Three Forks samples.