Rojas, Pedro A. Romero (Weatherford International) | Cristea, Alexandrina (Weatherford International) | Pavlakos, Paul (Weatherford International) | Ergündüz, Okan (ARAR AS) | Kececioglu, Tayfun (ARAR AS) | Alpay, Server Fatih (ARAR AS)
Nuclear magnetic resonance wireline logging and data post-processing technologies are continuously evolving, making significant contributions to rock, fluid typing, formation evaluation and characterization of the near-wellbore zone. In heavy oil fields, however, nuclear magnetic resonance (NMR) logging is known to provide an underestimated permeability, poor reliable oil typing and thus poor oil saturation and viscosity determinations, especially when the evaluation is based only on the spectra of transverse magnetic relaxation times (T2) (one-dimension NMR) [Romero et al., 2009]. Several attempts have been made to improve NMR results, mostly with limited success [Fang et al., 2004], especially in separating the oil component from the contribution of other fluids to the T2 spectra. The main reason lies not necessarily in the selection of the data acquisition parameters and sequences for a single-frequency or multi-frequency tool, but in the way how the data is post-processed.
The present study refers to a well drilled through the Derdere formation, a limestone/dolomite heavy oil reservoir in Turkey. The NMR data was acquired in with a centralized, single-frequency wireline tool in a 6-in. borehole, drilled with water-based mud in a freshwater carbonate reservoir. The generated T2 log was analyzed in a traditional way to obtain the NMR total porosity and its partitions based on standard cutoff values. For the given 12 API oil gravity, reservoir temperature (76 °C) and gas-oil-ratio (GOR) the T2Oil peak appears around 170 ms, right from the T2 cutoff for limestones; therefore, no corrections were needed on the permeability calculated from the Timur-Coates and Schlumberger-Doll-Research (SDR) equations. In the present well, only a diffused separation between oil and free water could be observed on the T2 distribution log from field data.
In the broader concept of Artificial Intelligence, the newly proposed post-processing steps to obtain the oil saturation start by deconvolving the T2 spectra, using blind source separation (BSS) based on independent component analysis (ICA) [Romero, 2016; Romero Rojas et al., 2018]. Based on its T2 peak value —the expected T2Oil peak response— calculated from the prejob planner/simulator, the deconvolution results show that one specific independent component corresponds to the oil, from which the oil saturation was determined.
Results demonstrated the usefulness of NMR logging technology in the characterization and evaluation of this reservoir. Data post-processing based on BBS-ICA enable adequate differentiation between fluid components from T2 spectra. For the reasons above, NMR has been proposed for additional wells in the same field.
Summary Seismic attributes are powerful tools that allow interpreters to make a more comprehensive and precise seismic interpretation. In this paper, we apply an unsupervised multiattribute technique called Independent Component Analysis to reduce dimensionality and extract the most valuable information of multiple spectral magnitude components in order to make an unsupervised seismic facies classification of channel complexes located in the Moki A Formation, Taranaki Basin, New Zealand. Introduction Depending on the seismic attribute that we choose, different information can be extracted (Infante-Paez and Marfurt, 2017) from the seismic volume, thus, relying on only one attribute information lead to an incomplete seismic interpretation. For this reason, multi-attribute techniques such as Principal Component Analysis (PCA), Selforganizing Maps (SOM) are commonly used. Based on higher order statistics, Independent Component Analysis separates a multivariate signal into subcomponents which are independent of each other (Hyva rinen and Oja, 2000), thus extracting more valuable information than techniques such as Principal Component Analysis (PCA) which tends to mix geology.
Geng, Meixia (Institute of Geophysics and Geomatics, China University of Geosciences, and Department of Earth Sciences, Memorial University of Newfoundland) | Welford, J. Kim (Department of Earth Sciences, Memorial University of Newfoundland) | Farquharson, Colin G. (Department of Earth Sciences, Memorial University of Newfoundland) | Peace, Alexander L. (Department of Earth Sciences, Memorial University of Newfoundland)
We present 3-D inversion results of gravity gradiometry data over the Budgell Harbour Stock (BHS) intrusion, in northern-central Newfoundland, Canada, obtained using a probabilistic inversion method. We examine multiple density contrast models obtained by inverting the single component Tzz and by jointly inverting five independent components. The inversion results show that
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: 213B (Anaheim Convention Center)
Presentation Type: Oral
In situ stress is an important parameter in many aspects of rock mechanics and often displays significant variability. Among many statistics, dispersion, which denotes how scatter or spread a data group is, is an effective tool to quantify the amount of variability. One common measure of dispersion is the standard deviation. However, this is only applicable to scalar data, and a robust approach to calculate a scalar-valued measure of stress dispersion is still not clear. This is mainly because of the tensorial nature of stress, which renders classical statistics inapplicable.
As multivariate statistics are widely used to analyse stress tensor components, here we investigate the use of three widely used scalar-valued scatter measures in multivariate statistics- namely, generalised variance, total variation and effective variance - to help identify a suitable scalar-valued measure to characterise stress variability. We compare these scatter measures with Euclidean dispersion, a tensorial statistic defined by us previously.
We show how the multivariate scatter measures are linked to Euclidean dispersion, and through the use of synthetic stress data illustrate that, in particular circumstances, all these measures can effectively characterise the variability of stress data. However, we have identified that there is significant potential for the multivariate total variation to generate erroneous results, due to a lack of coordinate invariance in the formulation of this statistic. Therefore, to remain loyal to the tensorial nature of stress, and avoid potential misuse, we recommend using the tensor-related Euclidean dispersion for characterisation of in situ stress variability.
In situ stress is an important parameter for a wide range of endeavours, including rock engineering design, hydraulic fracturing analysis, rock mass permeability and earthquake potential evaluation (Amadei & Stephansson, 1997; Zoback, 2010; Latham et al., 2013; Matsumoto et al., 2015). Because of the inherent complexity of fractured rock masses in terms of varying rock properties, the presence of discontinuities and unclear boundary conditions (Matsumoto et al., 2015), stress in rock often displays significant heterogeneity (Martin, 1990). For all rock engineering endeavours the characterisation and quantification of stress variability is therefore important.
Tying detailed well log measurements to lower resolution but a really extensive 3D seismic data volumes is key to quantitative seismic interpretation. Ties using a poststack or prestack convolution model are routine, while supervised classification tying well data to seismic attributes using neural networks and geostatistics are also well established. However, unsupervised classification ties where the objective is to identify unknown patterns in the data is less well established. In this paper, we use an automatic learning Gaussian Mixture Model to statistically characterize the well logs, evaluate the probability distribution functions of different lithologies and then tie them to corresponding 3D seismic attribute volumes. We precondition our four-dimensional data by projecting onto two dimensions using Independent Component Analysis.
We apply this workflow to Diamond M Field within the Horseshoe Atoll Reef Complex, Scurry County, TX, and find the Gaussian Mixture Model is able to statistically characterize and resolve lithological variations seen in the logs. In particular, we are able to clearly distinguish between lithologies from six different wells in the region of interest. The final result is a probabilistic map that statistically measures the variability of the seismic lithologies from the well logs.
Decomposing linear mixtures or superpositions into their components is a problem occurring in many different branches of science, such as telecommunications, Seismology, and biomedical signal analysis. Blind source separation (BSS) in particular, deals with the case where neither the sources, nor the mixing matrix or process of
mixing are known, the only available data are the mixed signals. The standard approach to BSS is Independent Component Analysis (ICA).
In fact ICA is a statistical technique that represents a multidimensional random vector as a linear combination of nongaussian random variables -'independent components'- that are as independent as possible. In 3D seismic surveys involved in exploration operations, recorded time series in each dimension are taken to be independent in nature and behavior, which is a direct result of physical response of materials into which seismic waves penetrate. But as an observation one dimension is sometimes contaminated up to one fifth by information from another dimension, resulting an increase in SNR.
Here we have applied FAST-ica algorithm to a 3D seismic record sample to extract least dependent recordings for all three spatial dimensions. In order to test the reliability of the decomposition we used mutual information (MI) transfer between signals to confirm the result of outputs from FAST-ica algorithm as, least dependent components, leading to more accurate interpretations of petrophysical parameters.
Sea-states are assumed to be separated into independent components. A stochastic model based on the Markov process that includes a seasonal model is applied to these independent components. The propagation of a sea-state in the ocean is represented by the cross-correlation between present and past sea-states. The correlation is provided by sea-states with a given time step. A comparison of the occurrence probability and cross-correlation of the sea-states between simulations using our proposed stochastic model and the hindcast data shows that our proposed stochastic model and simulation scheme are valid. We observe a high sea area propagating from the west to the east in our simulations, as observed in actual seas. INTRODUCTION Sea-states used in the design and operation of ships and marine systems are provided in the form of statistical information from ocean waves and winds. A sea-state is represented by several parameters. For example, significant wave height or mean wave period is a sea-state parameter. The long-term occurrence probabilities and cross-correlations of sea-state parameters are commonly provided as wave tables, such as the Global Wave Statistics and the Waves and Winds in the North Pacific Ocean, which are constructed from a number of accumulated observation data and from hindcast data. These wave tables are useful for evaluating the lifetime of ships (Fukuda, 1969, Nordenström, 1973, Naito et al., 1998, 2006). Oceangoing cargo ships are becoming more sophisticated and larger. This is a result of the optimization of sea transportation. A more accurate evaluation of the structural strength, hydrodynamic performance, and operating cost is naturally desired, because these ships have a large influence on several aspects of safety and economy. For example, it is desirable to compare fuel consumption and arrival time, depending on the route, season, operating criteria. In general, wave tables that cover wide sea areas are not suitable for this purpose, and historical sea-state data on the sea routes where these ships sail is required.