CPRM - Geological Survey of Brazil Summary Aero-geophysical surveys acquire data that can be used for many applications including urban planning, agriculture, forestry, or in the case of CPRM - Serviço Geológico do Brasil (Geological Survey of Brazil), geological mapping of a region, conducted by. In this latter case, the geologic interpretation is based on visual pattern recognition performed by a human interpreter of a series of maps generated by the aerogeophysical survey. In this paper we apply Principal Component Analysis and K-means clustering to better understand and develop a more quantitative analysis of the the data from airborne gamma ray spectrometry. Introduction Aerogeophysical data have long been used to study crustal geologic structure and tectonic evolution (Rosa et al., 2014). Several authors show how airborne geophysical data can be correlated with superficial geology (Carneiro et al., 2012; Cracknell et al., 2015; Harris et al., 2015).
Although principal-component analysis (PCA) has been applied widely to reduce the number of parameters characterizing a reservoir, its disadvantages are well-recognized. The CDF-PCA was developed to reconstruct reservoir models by use of only a few hundred principal components. It inherits the advantage of PCA to capture the main features or trends of spatial correlations among properties, and, more importantly, it can properly correct the smoothing effect of PCA. Both object-based and multipoint- statistics-based models generate relatively more geologically realistic channel bodies compared with conventional two-point geostatistics-based techniques. However, conditioning such models to production data and correctly sampling the posterior probability distribution are challenging problems.
Cascaded principal component analysis for separation of up and down going wavefields in a zero offset VSP - an example from Kuwait. Summary While a number of techniques (Freire et al., 1988; Maraschini et al., 2016, Hinds et al., 1996), have been described in the literature for wavefield separation, the median filter (Haradage, 1985) is still used by most contractors the vast majority of the time. This paper demonstrates the benefits of a cascaded principal component analysis (PCA) approach. More accurate and robust wavefield separation will allow more information about earth properties and multiples to be extracted. Introduction The standard technique for separation of up and down going wavefields is the median filter.
Correlations between isofrequency amplitude traces from spectral decomposition provide a means of finding frequency notches induced by thin layers. Isofrequency traces tend to be strongly correlated between frequencies at spectral nulls; and amongst those that are not at those frequency notches. Spectral principal component (PC) amplitude attributes take advantage of this property, and are indicative of layer thickness. With proper trace scaling and spectral balancing, spectral PC amplitudes are independent of layer reflection coefficients. Layers with only odd and even pair reflection coefficients have distinctive spectral PC-thickness relationships in synthetic wedge models. Three spectral PC attributes individually delineate amplitudes from: 1) an isolated reflection not affected by tuning; 2) tuning of an even reflection pair; and 3) tuning of an odd reflection pair in a 3-D synthetic turbidite model.
Presentation Date: Tuesday, September 26, 2017
Start Time: 11:25 AM
Presentation Type: ORAL
Liu, Bo (King Fahd University of Petroleum and Minerals) | Nuha, Hilal (King Fahd University of Petroleum and Minerals) | Deriche, Mohamed (King Fahd University of Petroleum and Minerals) | Mohandes, Mohamed (King Fahd University of Petroleum and Minerals) | Fekri, Faramarz (Georgia Institute of Technology)
This work considers the data compression of sequential seismic sensor arrays. First, the statistics of the seismic traces collected by all the sensors are modeled by using the mixture model. Hence, a distributed Principle Component Analysis (PCA) compression scheme for sequential sensor arrays is designed. The proposed scheme does not require transmitting the traces, leading to a more efficient computation and compression compared with the conventional local PCA compression. Furthermore, an efficient communication scheme is developed for the sequential sensor array for delivering the local statistics to the fusion center. In this communication scheme, the sensors update and pass a data package consisting of cumulative variables. The size of the data package does not increase throughout the process, which is more efficient than the direct communication scheme. Finally, the performance of the proposed scheme is evaluated by using both real and synthetic seismic data.
Presentation Date: Tuesday, October 18, 2016
Start Time: 1:25:00 PM
Location: Lobby D/C
Presentation Type: POSTER
Huang, Weilin (China University of Petroleum–Beijing) | Wang, Runqiu (China University of Petroleum–Beijing) | Zhou, Yanxin (China University of Petroleum–Beijing) | Chen, Yangkang (University of Texas–Austin) | Yang, Runfei (University of British Columbia)
The principal component analysis (PCA) is an effective proper orthogonal decomposition (POD) method for data analysis. The target of the PCA is to reduce the dimensionality of a data set and retain the variance presented in the data set as much as possible. We assume the random noise and irregularly missing data are additive and uncorrelated with the signal, and utilize the PCA method to simultaneously reconstruct and de-noise seismic data. In fact, PCA is to find a lower dimensional optimal approximation of the initial data in the least-squares sense. However, the signal has a deflection to the optimal approximation in this lower dimensional space. For this reason, we derive a fine-tuned operator acting on the extracted principal components to make the reconstructed data closer to the signal. Application of this proposed improved method on synthetic and field seismic data demonstrates a superior performance comparing with the traditional PCA.
Presentation Date: Tuesday, October 18, 2016
Start Time: 1:00:00 PM
Presentation Type: ORAL
Siena, Martina (Politecnico di Milano) | Guadagnini, Alberto (Politecnico di Milano) | Della Rossa, Ernesto (eni S.p.A.) | Lamberti, Andrea (eni S.p.A.) | Masserano, Franco (eni S.p.A.) | Rotondi, Marco (eni S.p.A.)
We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesianclustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.
Seismic data are always contaminated with noise. Therefore, signal-to-noise ratio enhancement plays an important role in seismic data processing. This paper illustrates a robust principal component analysis (RPCA) method to suppress erratic noise that contaminates seismic data. The method operates in the frequency-space domain and relies on a robust low-rank approximation of the seismic data volume. We adopt a nuclear norm constraint that yields the low-rank approximation of the desired data while using an ℓ1 norm constraint to properly estimate the erratic (sparse) noise. The problem is then tackled via the first-order gradient iteration method with two steps of softthresholding. We illustrate the effectiveness of this method via synthetic examples.
Principal component analysis (PCA) is an important tool for multivariate analysis in statistics. The idea is to reduce the dimensionality of a data set while preserving as much variability of data variables as possible (Jolliffe, 2010). Let us consider to recover a low-rank matrix L from the observed data
D = L+E, (1)
where E is a matrix representing the additive error. If we assume E is composed by small random perturbations, an optimal estimate of L can be acquired via the following optimization problem
min | |E| |2/F
s.t. rank(L) = k , D = L+E. (2)
The problem can be efficiently solved via singular value decomposition (SVD) (Golub and van Loan, 1996). The observed data D can be decomposed into a group of eigen-images via the SVD. The low-rank component L can be described with a few eigen-images that are associated to the largest singular values. The error E, however, will have energy spread over all the eigen-images (Trickett, 2003).
A variety of methods based on PCA have been developed in seismic data processing. For instance, Ulrych et al. (1999) introduced a time domain matrix rank reduction method to eliminate incoherent noise from seismic records. A related family of methods, the Karhunen-Loeve transform, has also been introduced for the enhancement of the signal-to-noise ratio of prestack gathers (Al-Yahya, 1991).
Upscaling of gas transport in shales is challenging because of the multiple scales of transport processes. Rock characterization using nanometer-scale digital rock technologies can capture fundamental geometrical and transport properties, but the obtained information is usually highly localized and contains significant uncertainties. An effective upscaling method is thus needed to propagate the pore-scale information across multiple spatial scales. A modified dual-porosity model was proposed to study multiscale gas transport in shales. The model consists of two domains, a kerogen domain, and an inorganic matrix. Within kerogen, gas transport is dominated by molecular diffusion and nonlinear adsorption and desorption. Within inorganic matrix, gas transport is dominated by convection and diffusion. A mass-exchange-rate coefficient is used to describe gas transport between kerogen and inorganic matrix. The modified dual-porosity model was used to perform history matching of a pressure-pulse-decay experiment in the laboratory. The four input parameters were absolute permeability and diffusivity within inorganic matrix, mass-exchange-rate coefficient between kerogen and inorganic matrix, and gas desorption-rate coefficient within kerogen; these parameters were solved using nonlinear optimization. The long tail of the pressure decline curve was well-captured by the model, implying it accounted for both fast- and slow-transport mechanisms. Permeability enhancement resulting from slip boundary and Knudsen diffusion was limited due to the relatively high pressure. Sensitivity analysis was conducted to study the impact of input variation on model output. There was a competing relationship between convection and diffusion within inorganic matrix; fast convection hindered diffusive transport, while with slow convection diffusive transport significantly affected pressure decline. Therefore, diffusive transport within inorganic matrix cannot be simply ignored. The effects of gas transport within kerogen and between kerogen and inorganic matrix depended significantly on the transport rate within inorganic matrix; when convection within inorganic matrix was slow, the transport processes within kerogen did not affect pressure decline in the short term; in contrast, when convection within inorganic matrix was fast, the transport processes within kerogen significantly affected the pressure decline in the short term. Thus, the impact of the transport processes within the slower domain depends primarily on the transport rate within the faster domain; this is referred to as hierarchical dependence. The principal component analysis (PCA) method was applied to study the continuous movement of the pressure decline curve resulting from input parameter variation; increased convective and diffusive transport rates within inorganic pores expedited pressure decline; conversely, increased mass-exchange-rate and desorption-rate coefficients slowed the pressure decline in the short term, but expedited pressure decline in the long term, when convection within inorganic matrix was fast. The modified dual-porosity model successfully captured the pressure decline curve measured in the laboratory. The interaction and interdependence between different transport processes were interpreted using the mechanisms of competing relationship and hierarchical dependence. PCA simultaneously processed hundreds of parameter realizations and the corresponding pressure decline curves; the ergodicity requirement was thus satisfied and the principal components of continuous curve movement can be extracted. The new modeling and analysis methods can advance the understanding of multiscale gas transport and consequently benefit storage evaluation and production prediction for shale gas recovery.