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Results
Enhanced Dispersion Analysis of Borehole Array Sonic Measurements With Amplitude and Phase Estimation Method
Li, Wei (China University of Petroleum-Beijing) | Guo, Rui (China University of Petroleum-Beijing) | Tao, Guo (China University of Petroleum-Beijing) | Wang, Hua (China University of Petroleum-Beijing) | Torres-Verdín, Carlos (The University of Texas at Austin) | Ma, Jun (The University of Texas at Austin) | Xu, Chicheng (The University of Texas at Austin)
Summary We introduce a new non-parametric matched-filterbank spectral estimator, referred as Amplitude and Phase Estimation (APES), to perform dispersion analysis of borehole array sonic measurements. This method extracts the dispersion characteristics of all wave modes by applying an APES filter to array sonic spectral data and converting the estimated wavenumber to slowness. The implemented adaptive filter in APES ensures that the output signal be sufficiently close to a sinusoid with a designated wavenumber in space domain, which constrains the interference from other wavenumber components and suppresses the noise gain. Consequently, the resolution and signal-noise-ratio of dispersion analysis is significantly enhanced. Dispersion fitness functions processed with APES indicate clearer and narrower ridges with minimum presence of alias. At each frequency, dispersions of all modes can be identified without knowledge a priori of the exact number of modes. More importantly, the new method is not computationally intensive compared to existing dispersion analysis methods. Processing examples with synthetic and field data are presented and compared with the weighted spectral semblance (WSS) method to demonstrate the applicability and advantages of this method.
Abstract This paper introduces a rock typing method for application in hydrocarbon-bearing shale (specifically source rock) reservoirs using conventional well logs and core data. Source rock reservoirs are known to be highly heterogeneous and often require new or specialized petrophysical techniques for accurate reservoir evaluation. In the past, petrophysical description of source rock reservoirs with well logs has been focused to quantifying rock composition and organic-matter concentration. These solutions often require many assumptions and ad-hoc correlations where the interpretation becomes a core matching exercise. Scale effects on measurements are typically neglected in core matching. Rock typing in hydrocarbon-bearing shale provides an alternative description by segmenting the reservoir into petrophysically-similar groups with k-means cluster analysis, which can then be used for ranking and detailed analysis of depth zones favorable for production. A synthetic example illustrates the rock typing method for an idealized sequence of beds penetrated by a vertical well. Results and analysis from the synthetic example show that rock types from inverted log properties correctly identify the most organic-rich sections better than rock types detected from well logs in thin beds. Also, estimated kerogen concentration is shown to be the most reliable property in an under-determined inversion solution. Field cases in the Barnett and Haynesville shale gas plays show the importance of core data for supplementing well logs and identifying correlations for desirable reservoir properties (kerogen/TOC concentration, fluid saturations, and porosity). Qualitative rock classes are formed and verified using inverted estimates of kerogen concentration as a rock-quality metric. Inverted log properties identify 40% more of a high-kerogen rock type over well-log based rock types in the Barnett formation. A case in the Haynesville formation suggests the possibility of identifying depositional environments as a result of rock attributes that produce distinct groupings from k-means cluster analysis with well logs. Core data and inversion results indicate homogeneity in the Haynesville formation case. However, the distributions of rock types show a 50% occurrence between two rock types over 90 ft vertical-extent of reservoir. Rock types suggest vertical distributions that exhibit similar rock attributes with characteristic properties (porosity, organic concentration and maturity, and gas saturation). The interpretation method considered in this paper does not directly quantify reservoir parameters and would not serve the purpose of quantifying gas-in-place. Rock typing in hydrocarbon-bearing shale with conventional well logs forms qualitative rock classes which can be used to calculate net-to-gross, validate conventional interpretation methods, perform well-to-well correlations, and establish facies distributions for integrated reservoir modeling in hydrocarbon-bearing shale.
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > Texas > East Texas Salt Basin > Cotton Valley Group Formation > Bossier Shale Formation (0.99)
- North America > United States > Texas > Ardmore - Marieta Basin > Newark East Field > Barnett Shale Formation (0.99)
- (6 more...)
Petrophysical Properties of Unconventional Low-Mobility Reservoirs (Shale Gas and Heavy Oil) by Using Newly Developed Adaptive Testing Approach
Hadibeik, Hamid (The University of Texas at Austin) | Chen, Dingding (Halliburton Energy Services) | Proett, Mark (Halliburton Energy Services) | Eyuboglu, Sami (Halliburton Energy Services) | Torres-Verdín, Carlos (The University of Texas at Austin)
Abstract Pressure testing in very low-mobility reservoirs is challenging with conventional formation-testing methods. The primary difficulty is the over-extended build-up times required to overcome wellbore and formation storage effects. Possible wellbore overbalance or supercharge are additional complicating factors in determining reservoir pressure. This paper addresses the above technical complications and estimates petrophysical properties of low-mobility formations using a newly developed adaptive-testing approach. The adaptive-testing approach employs an automated pulse-testing method for very low-mobility reservoirs and uses short drawdowns and injections followed by short pressure stabilization periods. Measured pressure transients are used in an optimized feedback loop to automatically adjust subsequent drawdown and injection pulses to reach a stabilized pressure as quickly as possible. The automated pulse data is used to determine supercharge effects, formation pressure, and mobility via analytical models by analyzing the entire pressure sequence. A genetic algorithm estimates additional reservoir parameters, such as porosity and viscosity, and confirms results obtained with analytical models (reservoir pressure and permeability). The modeled formation pressure exhibits less than 1% difference with respect to true formation pressure, while the accuracy of other parameters depends on the number of unknown properties. As a quicker method to estimate reservoir properties, a direct neural-network regression of pulse-testing data was also investigated. Synthetic reservoir models for low-mobility formations (M < 1 μD/cp), which included the dynamics of water- and oil- based mud-filtrate invasion that produce wellbore supercharging were developed. These reservoir models simulated the pulse-testing methods, including an automated feedback-optimization algorithm that reduces the testing times in a wide range of downhole conditions. The reservoir models included both simulations of underbalanced and overbalanced drilling conditions and enabled the development of new field-testing strategies based on a priori reservoir knowledge. The synthetic modeling demonstrates the viability of the new pulse-testing method and confirms that difficult properties, such as supercharging, can be estimated more accurately when coupled with the new inversion techniques.
Abstract Rock typing in carbonate reservoirs is challenging due to high spatial heterogeneity and complex pore structure. In extreme cases, conventional rock typing methods such as Leverett's J-function, Winland's R35, and flow zone indicator are inadequate to capture the heterogeneity and complexity of carbonate petrofacies. Furthermore, these methods are based on core measurements, hence are not applicable to uncored reservoir zones. This paper introduces a new method for petrophysical rock classification in carbonate reservoirs that honors multiple well logs and emphasizes the signature of mud-filtrate invasion. The method classifies rocks based on both static and dynamic petrophysical properties. An inversion-based algorithm is implemented to simultaneously estimate mineralogy, porosity, and water saturation from well logs. We numerically simulate the process of mud-filtrate invasion in each rock type and quantify the corresponding effects on nuclear and resistivity measurements to derive invasion-induced well-log attributes, which are subsequently integrated into the rock classification. Under favorable conditions, the interpretation method advanced in this paper can distinguish bimodal from uni-modal behavior in saturation-dependent capillary pressure otherwise only possible with special core analysis. We successfully apply the new method to a mixed clastic-carbonate sequence in the Hugoton gas field, Kansas. Rock types derived with the new method are in good agreement with lithofacies described from core samples. The distribution of permeability and saturation estimated from well-log-derived rock types agrees with routine core measurements, with the corresponding uncertainty significantly reduced when compared to results obtained with conventional porosity-permeability correlations.
- North America > United States > Texas (1.00)
- North America > United States > Kansas > Finney County (0.49)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- North America > United States > California > Sacramento Basin > 2 Formation (0.99)
- North America > United States > Kansas > Panoma Field (0.94)
Improved Estimation Of Pore Connectivity And Permeability In Deepwater Carbonates With The Construction Of Multi-Layer Static And Dynamic Petrophysical Models
Diniz-Ferreira, Elton Luiz (Schlumberger) | Torres-Verdín, Carlos (PETROBRAS – Petróleo Brasileiro S.A. and The University of Texas at Austin)
Due to sea-level variations, cycles of sedimentation can often be recognized from well logs. It is possible to differentiate rock types based on such geological cyclicity; for petrophysical purposes we will refer to those rock types as fluid flow units. In the presence of thin layers, flow units can only be detected with core data. The cause of sea-level variation in this field is not well understood and remains a subject of study by geologists. Wells were drilled with both oil-base mud (OBM) and water-base mud (WBM). The oil bearing-zone of wells drilled with WBM gave rise to a conspicuous invasion profile on resistivity logs. It is possible to simulate this invasion profile in different layers and estimate their permeability. Conversely, wells drilled with OBM did not show a conclusive invasion profile in the oilbearing zone because of the lack of electrical resistivity contrast between oil and mud filtrate. Due to the complexity of the pore space and the spatial heterogeneity of the reservoir under consideration, conventional well-log evaluation seldom reproduces petrophysical properties consistent with core data. It is necessary to construct multi-layer petrophysical models based on geological information to improve the interpretation. A model that combined well logs and geological properties was key to select bed boundaries and to construct an earth model. The latter model was used to perform static and dynamic simulations - matching simulated resistivity, nuclear, and NMR logs with field measurements. Petrophysical properties estimated with those simulations were in agreement with core laboratory measurements. Interpretation was performed in the oil-bearing zone of three wells: two of them-Wells Η and Γ – were drilled with OBM, the remaining well-Well Χ – drilled with WBM (Table 1). It is not possible to perform a correlation between the evaluated wells using well-logs.
- South America (0.93)
- North America > United States > Texas (0.29)