Acoustic Impedance Data Research Articles

Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

This study integrates seismic inversion and rock physics techniques to evaluate the hydrocarbon potential of an offshore field in the Niger Delta. Five wells revealed three reservoir sands with favourable reservoir properties, including gross thickness (49.2–81.4 m), porosity (0.18–0.2), permeability (565–1481 mD), and water saturation (0.16–0.54). A robust wavelet extraction process was implemented to guide seismic inversion, and a well log-centric approach was employed to validate the resulting acoustic impedance data. Rock physics analysis established correlations between acoustic impedance (Zp), porosity, fluid content, and lithology, enabling the identification of hydrocarbon-filled sands, brine-saturated sands, and shales. These relationships enabled the discrimination of hydrocarbon-filled sands [5000–8000 (m/s)(g/cc)], from brine-saturated sands [5600–8400 (m/s)(g/cc)], and shales [5000–9000 (m/s)(g/cc)] within the inverted seismic data. The inverted acoustic impedance section showed a general increase with depth, reflecting the typical compaction effects in the Niger Delta. Analysis of the impedance distribution across horizon time slices revealed prospective zones with low impedance values [below 6300 (m/s)(g/cc)], particularly in horizons 1 and 2. These newly identified zones exhibit the strongest potential for hydrocarbon accumulation and warrant further investigation. This study demonstrates the effectiveness of using well log and rock physics constrained seismic inversion for hydrocarbon exploration in an offshore field in the Niger Delta.

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

Underwater Acoustic Propagation using Monterey-Miami Parabolic Equation in Shallow Water Kayeli Bay Buru Distric

Indonesia's geographical position is an advantage compared to other countries, both in terms of geoeconomics, geopolitics and geostrategy. For this reason, it is necessary to develop and use acoustic methods to describe underwater features, carry out underwater communications or to measure oceanographic variables at sea. This research was intended to provide an analytical and visual graphical description with the aim that it can be used for various purposes both in the research, military and other marine fields, as well as to analyze the influence of sediment and different frequencies on acoustic propagation patterns in shallow waters of Kayeli Bay. This research was conducted using CTD data from Kayeli Bay, which is a body of water in Buru Regency, Maluku Province and is located between 3° 15' 55'' – 3° 22' 50" S and 127° 01'35" – 127° 01' 35 "E, using the Monterey-Miami parabolic equation method using 4 types of sediment and 3 different frequencies as model input. From the results of this research it can be concluded that the propagation of sound waves in shallow seas is greatly influenced by the type of sediment and frequenty used. Changes in acoustic impedance at the bottom of the water and within the water column can significantly influence the behavior of acoustic waves in shallow water environments, and accurate acoustic impedance data are critical for effective ray tracing modelling.

Log-constrained inversion based on a conjugate gradient-particle swarm optimization algorithm

Well-logging-constrained impedance inversion is an effective process for predicting the thickness and bifurcation of coal seams. Wavelet changes in a complex region achieve the best match between the inverse and source wavelets, affecting the accuracy of the inversion solution and the ability to obtain accurate inverted acoustic impedance (AI) data. We have conducted the joint inversion of wavelet and AI data using iterative methods, which combined the conjugate-gradient (CG) method and particle-swarm-optimization (PSO) algorithm. The Marmousi AI model was used to prove the reliability of the method. The CG-PSO algorithm achieved excellent results compared with the statistical wavelet pickup method. The wavelet obtained by the CG-PSO algorithm is preferred for inversion operations. We applied a new method to invert field data and predict the thickness and bifurcation of coal seams in the karst region. The results find that the wavelet spectrum obtained by the CG-PSO matches the spectrum map of the coal seam in the Yuwang colliery. We determined the distribution of the thickness and bifurcation of the 101 panel, Yuwang Colliery, Yunnan Province. The average error of the predicted coal thickness is 0.17 m (14.4%), which verifies the feasibility and effectiveness of the method. The method provides insights into the AI inversion of constrained waves in complex regions.

Simultaneous model-based inversion of pre-stack 3D seismic data targeting a deep geothermal reservoir, Northwest Hungary

The well-known, traditional way to extend P-wave acoustic impedance data between and beyond the well log locations is the post-stack inversion of seismic data usually available in the surroundings of the boreholes. A relatively new trend in the seismic exploration is based on the pre-stack inversion of the seismic CDP gathers providing both the P- and S-wave acoustic impedance sections (and volumes), as well as the estimated density data. This methodology is often called as simultaneous model-based inversion and can be utilized not only for hydrocarbon exploration, but it might also be a useful tool for the investigation of geothermal resources. In this study, we will compare the results of post-stack and pre-stack acoustic impedance inversions utilizing the same seismic volume. We will demonstrate and analyze the inverted attribute sections (and some of their derivatives) obtained by the pre-stack algorithm in detail. Finally, we will draw the conclusions about the lithological discrimination of the studied complex carbonate geothermal reservoir located in the pre-Cenozoic basement of the Little Hungarian Plain.

Wind turbine noise modeling including aeroacoustic sources and propagation effects: comparison against field measurements

The study of wind turbine noise and its impact is of growing importance with the increase in the demand for green and clean energy. As it is known that wind turbine noise can be a cause of annoyance in the vicinity of wind farms it is beneficial to predict with certain accuracy the generated noise in the design phase itself. A crucial step is the validation of prediction models against field measurements (in-situ). This article presents a wind turbine noise prediction model that combines Amiet's theory to calculate trailing edge noise and turbulence interaction noise in free field with a wide-angle parabolic equation valid in moving media to account for the long-range acoustic propagation effects. The model considers the wind turbine as an extended noise source and the rotation effects (such as the convective amplification and Doppler effect) are taken into account. The predicted noise levels are compared to those obtained from a measurement campaign where acoustic, meteorological and ground impedance data have been recorded simultaneously. First, the sound source model is validated close to the wind turbines for different wind speeds and directions. Then, noise predictions are compared to SPL measurements at various distances from the sound source, between 350 and 1300meters.

Application of model-based acoustic impedance inversion in the prediction of thin interbedded coal seams: a case study in the Yuwang colliery, Yunnan Province

The Permian coal seams in eastern Yunnan and western Guizhou are thin, numerous, and staggered with other thin coal seams. Depicting the fine characteristics of coal reservoirs is pivotal for the safe and efficient exploitation of coal and coalbed methane (CBM), and is important for transparent mining. To improve the inverse resolution and accuracy of predicting reservoir thickness, this study used the model-based acoustic impedance (AI) inversion method that utilizes seismic and logging data. This method changes seismic data, reflecting stratigraphic interfaces, into AI data, reflecting lithologic structures. Moreover, it avoids the relevant assumptions of wavelets and reflection coefficients. Compared with other inversion methods, model-based AI inversion strengthens the description of thin reservoir horizontal and vertical changes. The results showed that comprehensively using intermediate-frequency seismic information and high-low-frequency logging data greatly broadens the seismic data frequency band and improves the dominant frequency of the reflected wave. Furthermore, the AI profile resolution and the prediction accuracy of the physical parameters for the target geological body can be improved. A cross-validation comparing the inverted thickness and measured thickness of borehole cores was applied to achieve fine prediction (error of appropriately 0.02–0.4 m), providing a basis for CBM development.

Enhancing reservoir porosity prediction from acoustic impedance and lithofacies using a weighted ensemble deep learning approach

Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consuming, expensive, and difficult to conduct. In addition, seismic inversion confronts problems of nonlinearity and has multiple solutions. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability, mapping directly from acoustic impedance and lithofacies data to porosity. To prove the point, in this paper, we trained an ensemble of DL models and then proposed a weight combination of every single trained model’s strength to improve the result. We evaluated the method's reliability using a number of metrics. Further, we compared it with traditional ones. The weighted ensemble resulted in a lower error than the simple ensemble and the single model. Its spatial distribution map showed the best connectivity with that of historical porosity. Finally, we tested our method's effectiveness using a dataset that was used in a previously published study. Our method improved the prediction of the latter.

SEISMIC-DERIVED ROCK TRUE RESISTIVITY (Rt ) REVISITED. PART II: REFORMULATION USING WYLLIE’S TIME-AVERAGE MODEL

This paper is the second of a three-part presentation. As highlighted in the previous paper (Part I, Widarsono Mendrofa, 2006), the main objective of the study is to re-evaluate the potential of acoustic impedance as a source of resistivity data. This essentially came from the very idea of extracting information of resistivity (Rt ), data that plays a very important role in the determination of water saturation in reservoir, from seismic-derived acoustic impedance (AI).As observed in the past view years, there have been a lot of efforts devoted to the extraction of water saturation information from seismic. However, as Widarsono Mendrofa (2006) put it, most of the efforts were mainly based on pattern recognition activities with little attention was given to the theoretical aspects of relationships between seismic signals and water saturation. The work reported in this threepart presentation is concentrated more as re-establishing (a reformulation of works reported in Widarsono Saptono, 2003; 2004) the theoretical relationship between resistivity and acoustic impedance.In the Part I (Widarsono Mendrofa, 2006), a reformulation between the classical Gassmann acoustic velocity model and shally sand models of Modified Simandoux and Hossin is presented. In the reformulation, a new resistivity function of acoustic impedance has been established. In principle, whenever acoustic impedance data from seismic has been made available resistivity data for the determination of fluid saturation can be estimated.Despite the theoretical correctness of the resistivity function presented in the Part I, practicallity is not the function’s best aspect. In other words, the resistivity function is not an easy one to be used practically. Various parameters (e.g. matrix moduli) have to be assumed, since the data cannot easily measured even in the laboratory. This is indeed the main reason why gassmann model, and others such as Biot, has not been used much in day-to-day practices such as log interpretation for porosity determination.Being aware of such difficulties, in 1954 M.R.J. Wyllie et al proposed their “time average” model (named after its proportional averaging of pore fluid, rock matrix, and shale transit time values to represent transit time of a fluid-filled porous medium) for any practical uses related to P-wave velocity in porous media. Due to its simplicity, the model, as well as its subsequent modifications, has been used extensively since then in some areas especially in log analysis for porosity determination. Considering this simplicity aspect, this three-part study also adopted Wyllie “time average” model into its reformulation works. This Part II paper presents the formulation using Wyllie and the two shally sand models following the same manner that was adopted and presented in the Part I paper.Summarily, the objectives of the works presented in this paper are:- To establish a model/method to obtain formation rock true resistivity (Rt ) from seismic-derived acoustic impedance (AI),- To provide correction/modification onto previous works reported in Widarsono Saptono (2003, 2004), and- To provide a simpler alternative to the resistivity function yielded from the reformulation works presented in Part I paper (Widarsono Mendrofa, 2006)

Acoustic impedance and lithology-based reservoir porosity analysis using predictive machine learning algorithms

Porosity prediction and analysis is crucial for reservoir delineation, characterization and well placement. Basically, porosity measurements are obtained from well logs and core samples. However, methods based on core samples and well logs are sometimes challenging, time-consuming and very expensive. In this paper we attempt to predict and analyze porosity using machine learning algorithms. Porosity prediction is performed as a supervised multiple regression problem. Every data point of the training sample from the study area is made of lithology and acoustic impedance which are defined as independent variables or predictors while measured value of porosity is the dependent variable, provided as the label to be predicted. Deep neural network, Random forest and Decision tree algorithms were subjected to training and learning of rich and proper features that are important for the prediction of porosity thus, good generalization ability is crucial to the successful training of a machine learning model. Therefore, we used several optimization functions to train the deep neural network in order to choose the best performing one, on which we retrained the model using k-fold cross-validation technique. Although all the algorithms showed very good performance, deep neural network proved to be the most efficient with 0.042% of the mean squared error as the learning loss and 0.051% of the training mean absolute error. Our result was further tested for effectiveness by using new set of non-labeled lithology and acoustic impedance data and the result was compared with measured porosity and the result showed 89% of the Pearson correlation. Compared with measured porosity, prediction results displayed accurate prediction of porosity within milliseconds thus saving the working time. The result of this study shows that machine-learning algorithms can be time saving and reliable alternative for reservoir porosity prediction and analysis.

Prediction and modeling of petrophysical parameters of deep-buried, low permeability glutenite reservoirs in Yubei area, Turpan-Hami Basin, China

The reservoir geological model can provide detailed reservoir characterizations for oil and gas development, thereby addressing the problems of sparse well patterns and lack of data for deep-underground reservoirs in the Yubei area. In this study, stochastic simulations based on geostatistical analyses with seismic attribute constraints were used to predict the physical properties of the Wutonggou Formation reservoirs and establish a fine 3D geological model. Log interpretation models were established to represent the longitudinal distribution of petrophysical parameters. Next, the structural framework of the strata was constructed based on seismic interpretations, and seismic attribute analysis was subsequently performed. Genetic inversion and neural network algorithms were used to obtain acoustic impedance data and predict petrophysical parameters. The planar distribution characteristics of the parameters were then clarified. Finally, petrophysical models were established using Gaussian random function simulation under the control of the facies model, which in turn was simulated via sequential indicator simulation. The model results were evaluated based on the well and production data, and the model parameters exhibited distribution patterns similar to the well data. The model parameters also corresponded well with production data, exhibiting a compliance rate of 90.98%. The model shows that the study area has a large range of reservoir depths and is generally oriented NW-SE. The Wutonggou Formation mainly developed oil-bearing reservoirs of different depositional types at the bottom of the third and first members, and the thickness of the depositions increased significantly with depth. The reservoirs, as a whole, are characterized by medium-low porosity and low permeability. Furthermore, compaction has led to significant reductions in porosity and the formation of microcracks in the glutenite of deep-buried reservoirs, which play important roles in improving reservoir permeability and hydrocarbon flow properties.

Use of rock‐physics analysis of well logs to determine compaction history of Cretaceous shales in the Rovuma basin, Offshore Mozambique

ABSTRACTCretaceous shales from the emerging gas province of the Rovuma basin are examined using well‐log data from two exploration wells. The P‐wave acoustic impedance (AIp) data were estimated, and then brittleness and ductility were assessed within the framework of the Reuss–Voigt limits for acoustic impedance. We then predicted the shale consolidation using a weighting function (Wc) which varies between 0 for the case of grains in suspension (the Reuss limit) and 1 for the case of consolidated rock or cemented shale (the Voigt limit). At the Reuss limit, the formation AIp is highly sensitive to pressure while at the Voigt limit the formation AIp is insensitive to pressure. For the wells in this study, most data plot close to the lower bound with Wc < 0.5, hence showing a significant sensitivity to pressure. Although the Cretaceous shales in the Rovuma basin are dominated by a mechanical compaction regime, some onset of chemical compaction is observed. The rock‐physics analysis showed that the same shale in these two exploration wells had very distinctive and contrasting elastic properties. The deeper well interval clearly shows a ductile shale while the shallower well shows a range of ductile to brittle behaviour matching with the onset of chemical diagenesis at temperatures >58°C. These differences in the development and rate of chemical diagenesis in the same formation are likely due to contrasts in the rate of burial and have important implications for future seismic exploration studies.

Integrated reservoir characterization based on a comprehensive study of the geological structure using 3D seismic survey

The 3D seismic survey is the undisputed leader among tools of identifying potential exploration targets and reservoir characterization. This paper shows surveys that are crucial in the exploration and development of significant amounts of hydrocarbon resources, and enables operator companies to map complex geological structures and select better drilling locations. The purpose of the research is to have better understandings of formations and update previous studies in the oil field of Mangyshlak Basin, Western Kazakhstan. The Main results are the acoustic impedance, Vp/Vs ratio, lithological and reservoir properties data. The quality controls and analysis of results show a good match with well logs and good recovery of seismic signal in inversion, but it should be improved in some areas. The results, from a scientific point of view, expand the already known geological and geophysical studies of the reservoir and improve the quality of interpretation using seismic methods in studying the sedimentation environment.

Seismic multi-attribute analysis for petrophysics reservoir prediction with probabilistic neural network in “FA” field

Oil and gas reserves are increasingly difficult to find due to more complex geological conditions. This complex condition causes difficulties in determining reservoir distribution. Therefore, a better method is needed to overcome these complex geological conditions. In this study, the petrophysics analysis by using the multi-attribute and the Probabilistic Neural Network (PNN) used to make reservoir distribution model on seismic horizontal slice. This multi-attribute method and Probabilistic Neural Network (PNN) that can search for correlation between seismic attributes and the data sought, for the prediction of property values from surrounding rocks. From this method, the distribution of porosity data with a correlation value of 0.52 was generated, water saturation with a correlation value of 0.73, and shale content with a correlation value of 0.58. Where the combination of porosity data, water saturation, shale content, and acoustic impedance (AI) data of inversion results can be a clue to identify reservoir distribution. From the porosity and saturation values, hydrocarbon dispersion can be made, wherein this study values were obtained between 0.01 0.03. This “FA” field has a reservoir between wells F-06, FA-05, FA-15, and FA-18 and spreads westward from wells FA-05, FA-15 & FA-18. The distribution of petrophysical parameters generated from the validation of well data using the multi-attribute method. This thing prove that Multi-attribute and neural network analysis can be used to determine predictions of porosity, water saturation, and shale content well and can be used for reservoir characterization.

Seismic data assimilation with an imperfect model

Data assimilation methods often assume perfect models and uncorrelated observation error. The assumption of a perfect model is probably always wrong for applications to real systems, and since model error is known to generally induce correlated effective observation errors, then the common assumption of uncorrelated observation errors is probably almost always wrong, too. The standard approach to dealing with correlated observation errors, which simply ignores the correlation, leads to suboptimal assimilation of observations. In this paper, we examine the consequences of model errors on assimilation of seismic data. We show how to recognize the existence of correlated error through model diagnostics modified for large numbers of data, how to estimate the correlation in the error, and how to use a model with correlated errors in a perturbed observation form of an iterative ensemble smoother to improve the quantification of a posteriori uncertainty. The methodologies for each of these aspects have been developed to allow application to problems with very large number of model parameters and large amounts of data with correlated observation error. We applied the methodologies to a small toy problem with linear relationship between data and model parameters, and to synthetic seismic data from the Norne Field model. To provide a controlled investigation in the seismic example, we investigate an application of data assimilation with two sources of model error—errors in seismic resolution and errors in the petro-elastic model. Both types of model errors result in effectively correlated observation errors, which must be accounted for in the data assimilation scheme. Although the data are synthetic, parameters of the seismic resolution and the observation noise are estimated from the actual inverted acoustic impedance data. Using a structured approach, we are able to assimilate approximately 115,000 observations with correlated total observation error efficiently without neglecting correlations. We show that the application of this methodology leads to less overfitting to the observations, and results in an ensemble estimate with smaller spread than the initial ensemble of predictions, but that the final estimate of uncertainty is consistent with the truth.

History Matching the Full Norne Field Model Using Seismic and Production Data

SummaryIn this paper, we use a combination of acoustic impedance and production data for history matching the full Norne Field. The purpose of the paper is to illustrate a robust and flexible work flow for assisted history matching of large data sets. We apply an iterative ensemble-based smoother, and the traditional approach for assisted history matching is extended to include updates of additional parameters representing rock clay content, which has a significant effect on seismic data. Further, for seismic data it is a challenge to properly specify the measurement noise, because the noise level and spatial correlation between measurement noise are unknown. For this purpose, we apply a method based on image denoising for estimating the spatially correlated (colored) noise level in the data. For the best possible evaluation of the workflow performance, all data are synthetically generated in this study. We assimilate production data and seismic data sequentially. First, the production data are assimilated using traditional distance-based localization, and the resulting ensemble of reservoir models is then used when assimilating seismic data. This procedure is suitable for real field applications, because production data are usually available before seismic data. If both production data and seismic data are assimilated simultaneously, the high number of seismic data might dominate the overall history-matching performance.The noise estimation for seismic data involves transforming the observations to a discrete wavelet domain. However, the resulting data do not have a clear spatial position, and the traditional distance-based localization schemes used to avoid spurious correlations and underestimated uncertainty (because of limited ensemble size), are not possible to apply. Instead, we use a localization scheme that is based on correlations between observations and parameters that does not rely on physical position for model variables or data. This method automatically adapts to each observation and iteration.The results show that we reduce data mismatch for both production and seismic data, and that the use of seismic data reduces estimation errors for porosity, permeability, and net-to-gross ratio (NTG). Such improvements can provide useful information for reservoir management and planning for additional drainage strategies.

CO2 storage capacity estimation through static reservoir modelling: A case study of the lower Cretaceous Gage Sandstone reservoir in the offshore Vlaming Sub-basin, Perth Basin, Australia

The Lower Cretaceous Gage Sandstone is a deep saline aquifer which is overlain by the regionally extensive Lower Cretaceous South Perth Shale seal in the offshore Vlaming Sub-basin, Perth Basin, Australia. This paper is focused on the CO2 storage capacity estimation in the Gage Sandstone reservoir by integrating both the well and seismic data. After a 3D grid system was constructed, well log interpretations, depth converted interval velocity and seismic relative acoustic impedance data were imported into the 3D grids. The volume fraction of shale was first constructed combining the neural networks modelling and residual stochastic simulation from the well and seismic attributes data. Porosity was modelled using sequential Gaussian co-simulation with the volume fraction of shale model. The CO2 storage capacity was estimated using the total pore volume and storage coefficients in the US-DOE methodology. The best estimate (P50) of carbon storage capacity in the Gage Sandstone reservoir is 493 million tonnes based on the static reservoir modelling.

Linking Geostatistical Methods: Co-Kriging – Principal Component Analysis (PCA); with Integrated Well Data and Seismic Cross Sections for Improved Hydrocarbon Prospecting (Case Study: Field X)

In this era of globalization, the demand for energy is rising in tandem with social and economic development throughout the world. Current hydrocarbon demand is much greater than domestic crude oil and natural gas production. In order to bridge the gap between energy supply and demand, it is imperative to accelerate exploration activities and develop new effective and efficient techniques for discovering hydrocarbons. Therefore, this study presents a new method for integrating seismic inversion data and well data using geostatistical principles that allow for the high level of processing and interpretation expected nowadays. The main part of this paper will concern the preparation and processing of the input data, with the aim of constructing a map of hydrocarbon-potency distribution in a certain horizon. It will make use of principal component analysis (PCA) and the co-kriging method. In the case study of Field X, we analyze a single new dataset by applying PCA to every existing well that contains multivariate rock-physics data. The interpretation that can be extracted from the output gives us information about the hydrocarbon presence in a particular depth range. We use that output as our primary dataset from which our research map is constructed by applying the co-kriging method. We also rely on an acoustic impedance dataset that is available for a certain horizon to fulfill the co-kriging interpolation requirement. All of the acoustic impedance data and output data that result from the application of PCA in a particular horizon give strong correlation factors. Our resulting final map is also validated with information from proven hydrocarbon discoveries. It is demonstrated that the map gives accurate information suggesting the location of hydrocarbon potency, which will need some detailed follow-up work to enhance the distribution probabilities. This method can be considered for hydrocarbon prediction in any area of sparse well control.

An assessment of a conical horn waveguide to represent the human eardrum

This study examined a model of the acoustic input impedance of the ear that includes a waveguide model of the eardrum. The eardrum was modeled as a lossless conical-horn with rigid walls. The ear canal was modeled as a one-dimensional lossy transmission line. The output impedance of the eardrum, the middle ear, and the cochlea, was modeled as a circuit analog. The model was fit to acoustic input impedance data from human ears using a nonlinear least-squares fit. The impact of a conical-horn shape for the eardrum was quantified by comparison with the eardrum modeled as a near-flat surface. The model provided a good match to the data over the frequency range examined. A conical-horn model of the human eardrum provided gain at high frequencies, most notably above 1–2 kHz, with a broader middle-ear frequency response. This finding may suggest that eardrum shape plays an important role in sound transmission to the cochlea.

Characterization of Architectural Elements of Ordovician Fractured-cavernous Carbonate Reservoirs, Tahe Oilfield, China

ABSTRACT A new method for characterizing architectural elements of fractured-cavernous carbonate reservoirs was proposed, with application to an Ordovician reservoir in the Tahe Oilfield, Tarim basin. The new method integrates observations from outcrop analogs and concepts from modern karst theory for the mapping and modeling of fractured-cavernous reservoirs. In this paper, fractured-cavernous reservoirs were divided into 4 architectural element types—underground river caverns, sinkholes, discrete internal caverns, and faults. Architectural elements of the Ordovician reservoir in Tahe oilfield were identified and characterized by integrating well logs and seismic data. A new method constrained by faults, karst zones, and seismic acoustic impedance data, was introduced to build a 3D model of architectural elements of fractured-cavernous reservoir in the S48 unit of Tahe Oilfield. A porosity model was then derived from the architectural element model using facies-constrained method. The research provides a work-flow for the characterization of fractured-cavernous reservoirs and determining optimal methods for maximizing oil recovery in the study area or in similar areas.