Evaluation Of Hydrocarbon Reservoirs Research Articles

New development of sensitivity improvement for compensated neutron porosity tool in gas-filled boreholes

Porosity is one of the most important indicators used for hydrocarbon reservoir evaluation and extraction. However, obtain accurate formation porosity measurements in gas-filled boreholes can be challenging, this is because gas cannot slow down neutrons as effectively as water due to its lower hydrogen index, which results in measurement sensitivity reduction and consequently impacts the obtained porosity. One way to address this issue is loading water into the borehole which is commonly referred to as water loading. However, this process significantly increases operational time and cost, as well as shielding perforated gas zones. Therefore, a new strategy is proposed to improve the sensitivity of porosity measurement in gas or air-filled boreholes without the need for water loading. The key to this strategy is to incorporate a sleeve design outside the tool to enhance its performance in gas-filled intervals. The sleeve is composed of a downhole applicable material whose neutron slowing down ability is close to water. The optimized deployment orientation of the sleeve could then be determined through simulation. In this work, three potential sleeve materials are identified and compared based on an existing neutron porosity tool. The impacts of different tool-sleeve-borehole orientations are analyzed to determine the most effective design strategy. A series of test pit experiments is conducted to validate the feasibility of the design.

Automated lithology classification from drill core images using convolutional neural networks

In hydrocarbon reservoir evaluation, lithology is a key characteristic for determination of storage capacity and rock properties. Lithology is usually predicted from well log data or directly identified through manual inspection by geologists during core logging. Automatic prediction of lithology has been a focus of many studies inspired by rapid developments in machine learning. Studies mostly focus on predicting lithofacies from well logs using neural networks, and more recently from borehole image logs using convolutional neural network (CNN). Although core is more representative of reservoir lithology than well log data, employing machine learning for automatic lithology identification from core is limited. A CNN-based approach is developed to classify core images into three lithologies: sandstone, limestone, and shale. The model is based on ResNeXt-50 architecture, which had better performance in lithology prediction than ResNet-18 and Inception-v3 architectures. Our method processes core tray images and outputs lithology logs in a fully automated procedure and predicts the lithologies of new (unseen) core with an accuracy of 93.12%.

Cluster analysis of core measurements using heterogeneous data sources: An application to complex Miocene reservoirs

An integrated clustering approach is suggested for the interpretation of petrophysical properties measured on core samples. Porosity, carbonate content, bulk and grain density, permeability, irreducible water saturation and capillary pressure curves measured from different sources are processed simultaneously for a more reliable evaluation of hydrocarbon reservoirs. The specialty of the problem is that the input dataset is composed of observations at different boreholes and depth intervals, where the number of rock specimens and measurement types strongly vary. Several statistical methods such as cluster and factor analyses are traditionally used for rock typing, which are either highly limited or not feasible at all for the processing of such incomplete datasets. To overcome this difficulty, the sparse matrix of multivariate observations is fully filled with reliable estimates of petrophysical parameters before cluster analysis. In the first phase, a correlation-based interpolation method is proposed for replacing the missing data with synthetic ones estimated from the available petrophysical information. Then, principal component analysis of the filled data matrix is performed to investigate the relative contribution of different variables to the solution and reduce the large number of measured parameters into fewer variables. In the last step, non-hierarchical cluster analysis of the principal components is made to separate the lithological units and reservoir zones. The suggested statistical workflow is tested in a Hungarian oilfield, where Miocene reservoirs of different lithologies are evaluated using a large amount of laboratory data collected for three decades. When clustering all petrophysical variables in a joint procedure, not just the lithological properties but also the fluid and other reservoir characteristics are taken into account to differentiate the pay zones from unproductive intervals. In addition to the current application, the statistical method may serve as useful tool for improved well log analysis, well-to-well correlation and reservoir modeling on larger scales.

Hydrocarbon Reservoir Evaluation: a case study of Tymot field at southwestern offshore Niger Delta Oil Province, Nigeria

Abstract: Aim: This study presents the log analysis results of a log suite comprising gamma ray (GR), resistivity (LLD), neutron (PHIN), density (RHOB) logs and a 3D seismic interpretation of Tymot field located in the southwestern offshore of Niger delta. This study focuses essentially on reserves estimation of hydrocarbon bearing sands. Well data were used in the identification of reservoirs and determination of petrophysical parameters and hydrocarbon presence. Three horizons that corresponded to selected well tops were mapped after well-to-seismic tie. Structural depth maps were created from the mapped horizons. The structural style is dominated by widely spaced simple rollover anticline bounded by growth faults, and this includes down-to-basin faults, antithetic faults and synthetic faults. The petrophysical values – the porosity, net-to-gross, water saturation, hydrocarbon saturation that were calculated yielding an average porosity value of 0.23, water saturation of 0.32 and an average net-to-gross value of 0.62. Three horizons H1, H2 and H3 were mapped. The three horizons marked the tops of reservoir sands and provide the structures for hydrocarbon accumulation. Hydrocarbon in-place was estimated. The total hydrocarbon proven reserves for the mapped horizons H1, H2, and H3 were estimated to be 39.04MMBO of oil and 166.13BCF for sand E.

An introduction to wireline log analysis by integration of ascendant hierarchical clustering and k-nearest neighbor methods for permeability prediction using conventional well logs and core data

Permeability is a key parameter in the evaluation of hydrocarbon reservoirs. Conventionally, the core data analysis is utilized to determine the permeability. However, core analysis method is time-consuming and expensive. In this paper, we present an approach for permeability prediction from conventional well logs and core data. Our approach integrates two methods, the ascendant hierarchical clustering, which is used to classify the well log responses into relatively homogeneous subgroups based on electrofacies, and the k-nearest neighbor method, to predict the permeability from the nearest neighbors that belong to a similar subgroup. The procedure is applied on one of the Iranian South West oil reservoirs. The results of this study show that integration of well logs and core data based on electrofacies can reliably predict permeability.

Seismic interpretation of tectonic and paleogeomorphologic controls on sediment dispersal patterns in a continental rift basin: A case study from the Bohai Bay Basin, China

Tectono-paleogeomorphologic frameworks exert a major control on sediment dispersal patterns and types of depositional systems, both of which influence reservoir geometry and quality. In this study, we analyze the framework of the [Formula: see text] Member of the Dongying Formation in QHD29-2 Block, Bohai Bay Basin, China. The framework is constructed through (1) conventional sedimentologic study of single-well facies, cumulative frequency curves, and thin section analysis, (2) seismic stratigraphic interpretation of sectional seismic facies, and (3) seismic sedimentologic mapping through stratal slicing and seismic multiattribute analysis. The structural system is marked by a master boundary fault in the Shijiutuo Uplift that has greater displacement at its center than at the propagating fault tips. The resulting paleogeomorphologic architecture is characterized by a steep slope in the central part of the boundary fault and two gentle slopes at fault tips. Sediment dispersal patterns indicate that fan-shaped and lobate proximal fan deltas were rapidly deposited in the central steep slope and belt-shaped distal river deltas were deposited in the gentle slopes after long-distance transport. Proposed sediment dispersal patterns for the central fault segment and the propagating fault tips should serve as a useful reference for the stratal geometry, depositional patterns, distribution of depositional facies and of potential reservoir and source rocks, and evaluation of hydrocarbon reservoirs in extensional rift basins in eastern China and similar basins around the world.

Application of wavelet transform for evaluation of hydrocarbon reservoirs: example from Iranian oil fields in the north of the Persian Gulf

Abstract. The Persian Gulf and its surrounding area are some of the biggest basins and have a very important role in producing huge amounts of hydrocarbon, and this potential was evaluated in order to explore the target for geoscientists and petroleum engineers. Wavelet transform is a useful and applicable technique to reveal frequency contents of various signals in different branches of science and especially in petroleum studies. We applied two major capacities of continuous mode of wavelet transform in seismic investigations. These investigations were operated to detect reservoir geological structures and some anomalies related to hydrocarbon to develop and explore new petroleum reservoirs in at least 4 oilfields in the southwest of Iran. It had been observed that continuous wavelet transform results show some discontinuities in the location of faults and are able to display them more clearly than other seismic methods. Moreover, continuous wavelet transform, utilizing Morlet wavelet, displays low-frequency shadows on 4 different iso-frequency vertical sections to identify reservoirs containing gas. By comparing these different figures, the presence of low-frequency shadows under the reservoir could be seen and we can relate these variations from anomalies at different frequencies as an indicator of the presence of hydrocarbons in the target reservoir.

Reservoir rock permeability prediction using support vector regression in an Iranian oil field

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. It is often measured in the laboratory from reservoir core samples or evaluated from well test data. The prediction of reservoir rock permeability utilizing well log data is important because the core analysis and well test data are usually only available from a few wells in a field and have high coring and laboratory analysis costs. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data; however, these correlation formulae are not universally applicable. Recently, support vector machines (SVMs) have been proposed as a new intelligence technique for both regression and classification tasks. The theory has a strong mathematical foundation for dependence estimation and predictive learning from finite data sets. The ultimate test for any technique that bears the claim of permeability prediction from well log data is the accurate and verifiable prediction of permeability for wells where only the well log data are available. The main goal of this paper is to develop the SVM method to obtain reservoir rock permeability based on well log data.

Structure and kinematics of an outcrop-scale fold-cored triangle zone

Triangle zones are widespread structural elements that link fold and thrust belts with their foreland basins. We present a structural analysis of an outcrop-scale (~6 m [20 ft] wide) triangle zone that is exposed in the siliciclastic Carboniferous strata of the Harz Mountains in northern Germany. The geometry of the triangle zone is critically compared with larger outcrop and seismic-scale structures. The external form of the triangle zone is the same as that proposed for larger examples, with two bounding detachments that dip in opposing directions. However, the interior of the triangle zone is characterized by tight to isoclinal folds. We demonstrate how the triangle zone probably evolved from a fault-bend fold, which accreted further folds behind it. This is an alternative fold-based interpretation for a structure that is commonly modeled as a duplex stack. We present the resulting consequences for seismic interpretation and hydrocarbon reservoir evaluation.

A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN)

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. There are a lot of well log data related with this parameter. In this study, permeability is predicted using them and a supervised committee machine neural network (SCMNN) which is combined of 30 estimators. Statistically speaking, the results of simple neural network are not proper, for permeability data have a large range and are multi-populations. Therefore, all of data were divided in two low and high permeability populations using statistical study. Each estimator of SCMNN was combined of two simple networks to predict permeability in both low and high classes and one gating network, considered as a classifier, classified data to these two classes. Thus, each low and/or high input data would predict in related network. This SCMNN was used to predict permeability on the data of one of petroleum reservoirs of south-west of Iran. 220 samples of this reservoir were available that 80% of them were used as training data and 20% of them were used as validation and testing data. The overall fitting between predicted permeability versus measured ones was qualified through R 2 (R = correlation coefficient) to be 97.72% which is considered appropriate. Whereas, R 2 in the simple network in the best stat was 84.14%. The high power and efficiency of SCMNN are indicated by lower bias and better R 2 in results.

Rock physics investigation of seismic wave absorption in reservoir rocks

This research provides insight into the process of pore- fluid induced absorption which is important for absorption compensation and seismic image enhancement. The absorptive property of a medium is described by the rock inverse quality factor ( ), perturbational forward modeling is conducted by varying saturation and estimating the inverse quality factor using first principles of rock inelastic properties and modeling seismic absorption in rock with partial gas saturation as commonly encountered in hydrocarbon exploration. The response of a visco-elastic rock depends on the frequency of the propagating seismic energy. The difference between modulus estimated at high and low frequencies is translated to the coefficient of absorption (or inverse quality factor). Absorption estimated at 0.4 irreducible water saturation (0.6 gas saturation) is higher than 0.8 irreducible water saturation (0.2 gas saturation). The higher the gas saturation, the higher the absorption, and the poorer the seismic image. The signature of gas induced absorption in the selected earth model are the increase time –thickness in the reservoir interval, large contrast in the top and base amplitude, pulse broadening and wavelet distortion. In hydrocarbon reservoir evaluation, seismic absorption can be used to interpret for fluid units. Information about seismic absorption can also be used, by means of absorption compensation, to enhance seismic data resolution.KEYWORDS: Saturation, Absorption, Dispersion, Inverse Quality Factor, Anelastic

Multiple permeability predictions using an observational learning algorithm

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. Well log data are frequently available to infer this parameter along drilled wells. Many fundamental problems remain unsolved by most predictive models. This paper introduces the use of an improved neural network trained by an observational learning algorithm to provide solutions for two particular problems: the generation of additional or “virtual” samples when the number of training data is insufficient; and the generation of multiple permeability values at the same reservoir depth for reliability analyses. The methodology is illustrated by a case study in western Australia. Four drilled wells with well logs and core permeability are used in this study. The data from the first two wells are used for training, while the others are used as unseen data to test the performance of the model. The results show that the proposed method gives smaller error compared to multiple linear regression and other neural networks (simple committee networks and bootstrap aggregating). It also provides valuable information on the reliability of the permeability predictions which is consistent with the geological studies.

ABSTRACT: Evaluation of hydrocarbon reservoirs within coal-shale sequence of Geleki field: A case study

ABSTRACT: Evaluation of hydrocarbon reservoirs within coal-shale sequence of Geleki field: A case study

Poisson’s ratio — an interdisciplinary link?

The collaboration of geophysicists, geologists, and reservoir engineers is of utmost importance in hydrocarbon reservoir evaluation and development. An important aspect of this collaboration is the derivation of critical rock properties, such as Poisson’s ratio, from seismic velocities obtained from well logging. Unfortunately, applicability of velocity‐based rock properties to reservoir evaluation is suspect. Response of a rock layer to the dynamic stresses imparted by the passage of a seismic wave may differ significantly from the response to static stresses due to overburden and pore fluid. It is, of course, the response to static stresses that is critical in reservoir development.