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Abstract Uncertainties in basic petrophysical parameters in shale gas reservoirs are discussed in this paper. These parameters include porosity, permeability, gas adsorption, water saturation and TOC. Current available methods to evaluate shale gas petrophysical properties are summarized. These methods include core laboratory measurements and well logs. Sources of uncertainty in these measurements are discussed. Uncertainty analysis using commercial simulation software was performed to understand how these uncertainties influence reservoir simulation results and predictions. Sensitivity and uncertainty studies in this paper are based on typical ranges of shale gas properties extracted from published evaluation reports or open data. Results from this study will help guide the focus of future research and technology development for shale gas reservoirs.
- North America > United States > Texas (0.47)
- North America > Canada > Alberta (0.29)
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- North America > United States > Arkansas > Haynesville Shale Formation (0.99)
- (9 more...)
Abstract Determining effective porosity and permeability (filtration capacity properties-FCP) of reservoir rocks represented by thinly laminated depositional sequences is a challenging task. The vertical resolution of most logging tools is coarser than the thickness of individual lamina; hence, these tools usually record averaged formation properties. In the case of NMR measurements, the best vertical resolution (VR) provided by modern NMR tools is 15 cm - 30 cm (VR=1 ft). To enhance the NMR vertical resolution, geological information with a higher VR is required. Electrical micro-imaging tools with VR=0.2?? (0.5cm) can provide high-resolution data which can be used to qualitatively determine the inter-bedding structure in thinly laminated intervals. The integration of these two measurements provides the physical link between detailed lithological data obtained by borehole image measurements and FCP estimated by the NMR tool. The T2 spectra obtained from the majority of water saturated clastic rocks, with negligible diagenetic effects, is mainly a function of grain size distribution (GSD) and depositional environment (i.e. function of pore size distribution). Based on the physical relationship between pore size and grain size in clastic reservoir rocks, the FCPs are mainly controlled by grain size distribution. The new Main Spectral Components (MSC) algorithm for Image-NMR data processing and integration has been developed. The MSC has been used to define the main groups of pores from pore size distributions representing the studied porous media. In this paper, we used the integration of borehole image, NMR data and GR logs as a basis for reservoir rock clustering to predict porosity and permeability with high resolution. Borehole electrical image analysis is used for the identification of clay type distribution (laminated or dispersed) and for preliminary rocks' classification. The developed MSC technique is efficiently used for main geological clusters identification with simultaneous estimation of their FCPs. The newly developed Image-NMR integration technique required the following input data: Statistical distributions of conductivity within the limit of NMR Vertical Resolution The correlations between MSCs determined from NMR spectra and statistically identified MSCs determined based on the borehole image conductivity histograms; and The correlations between T2 values and grain size (GSD) established for the main clusters of clastic rocks These relationships were used for computing T2 values with following prediction of GSD with vertical resolution equivalent to the VR of resistivity image logs. This computation is based on the assumption that each interval (in the limit of the borehole image's VR) is presented by one single pure cluster according to grain size/pore size distribution. The generated high resolution T2 data was then used for the construction of high resolution grain size distribution curves. The obtained GSD data can be used as an input for effective porosity and permeability estimation using a forward modeling technique.
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
Summary Petrophysical data requires drill holes, and drilling is costly, so geophysics serves to "connect the dots." Although a dense drilling program may be called for in evaluating a mineral property, this study indicates models derived from aeromagnetic data with constraints from geological knowledge and no petrophysics may be fairly accurate. They may be only slightly changed using subsequent borehole information. In addition, the use of 2D inversion on 3D structures may produce situations where acceptable inverse fits are unattainable. The most preferable inverse model may fit curve shape, but be offset from it.
SUMMARY Petrophysical information has proven valuable in joint inversion. However, current joint inversion algorithms usually cannot deal with the situation where multiple petrophysical relationships exist in one area, because it is nearly impossible to correctly specify beforehand the spatial applicability of these multiple petrophysical relationships. We propose a general framework for joint inversion constrained by a priori petrophysical information. We demonstrate through three synthetic examples that this new method can effectively deal with the existence of multiple petrophysical relationships, and can generate inverted models that are consistent with all geophysical and petrophysical data.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
If today challenges: deepwater presalt, recession produced a severe drought you are studying the Higgs boson, ultratight rock, thin-bed, coalseam, period for oil industry professionals, chances are that the particle physics and gas hydrates. Activities in leading to today's oil and gas background you have will allow you these plays have already led many industry's ongoing massive crew to better understand how neutron and professionals to rethink decades-old change, with the industry's mainstay hydrogen particles work in different concepts, such as pay thickness or professional workforce going into logging environments.
Introduction In working with organic shales the information about rock composition is generally supplied in a mix of volume percentages and weight percentages. Total organic carbon (TOC) is typically supplied as a weight percentage of the total sample weight, Fourier transform infrared spectroscopy derived (FTIR) mineralogy as a weight percentage of the samples inorganic weight, and porosity is defined as a volume percentage. In building regressions and combining datasets it is generally necessary to convert to a consistent composition reference system. In this note ϕk, the porosity due to the organic material is expressed in terms of the intrinsic porosity of the organic material ϕ ki, the mass fraction of the organic material Fo, the matrix density of the organic material, Pk, and the bulk density of the inorganic material Pib. This expression is used with experimental data of the methane porosity, TOC, and realistic estimates for Pk and Pib to estimate the intrinsic organic porosity. Five Barnett Shale samples from a single well all had intrinsic organic porosity of approximately 30%. A high porosity is consistent with ion-milled SEM images of Barnett Shale samples (Passey et al., 2010, Sondergeld et al., 2010).
Technology Focus The times they are a-changin’, Bob Dylan sang in the mid-1960s. And it seems to be the case in our industry today. With the never-ending search for new sources of hydrocarbons in more-complex environments, the application of old technologies has not been satisfactory to help us fully realize the new exploration opportunities. As the oil industry endeavors to extract hydrocarbons from fields where, only a few decades ago, it seemed impossible, engineers and scientists are tasked with developing ideas to gain an understanding of the dynamic behavior of the reservoirs, sometimes requiring redundant information. Creativity and ingenuity are called upon when facing challenging projects. Both are particularly necessary when one is trying to extract as much information as possible by the clever integration of multiple groups within an organization and manipulation of complementary data sets. Despite the difficulties encountered in unconventional plays or in tight carbonates that normally require stimulation, along with patience and long-term commitment, the application of sound engineering principles has opened new areas around the world that, until recently, were perceived as not being economical. Two of the key parameters required for full exploitation of a hydrocarbon-bearing field—rock permeability and reservoir pressure—are probably the most elusive to determine in the exploration phase, particularly so in unconventional plays. Hence, any technique that decreases the uncertainty in those estimates is a new valuable tool to the engineers involved in the exploration of new frontiers. Once the size and properties of the container have been evaluated, knowledge of the fluid properties is required to under-stand the flow characteristics. In the early stages of developing a field, on the other hand, engineers and geoscientists continue to integrate as many data sets as possible to quantify the volume of hydrocarbons that may be produced. The well-test data, however, confirm whether the hydrocarbons are producible. With this information, field development can start in earnest. The integration of the static and dynamic data then provides encouragement to develop a field. The three articles chosen are good examples of data integration to understand a field or the clever use of well-test data to solve a specific problem. The interested reader should also check the three alternative articles, as well as other articles available in the OnePetro library (www.onepetro.org). Recommended additional reading at OnePetro: www.onepetro.org. SPE 159126 Reservoir-Connectivity Analysis Using Long-Term Interference Testing in a Waterflood Pilot in the Carbonate Marrat Formation of the Greater Burgan Field, Kuwait by Naz Gazi, Kuwait Oil Company, et al. SPE 159503 Sampling While Drilling: An Emerging Technology by Steven Villareal, Schlumberger, et al. SPE 159680 Investigation of Thermal Well-Test Analysis for Horizontal Wells in SAGD Process by Ashkan Jahanbani Ghahfarokhi, Norwegian University of Science and Technology, et al.
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Wara Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Ratawi Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Mauddud Formation (0.99)
- (3 more...)