Uncored Wells Research Articles

A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin

Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field.

Permeability Prediction By Classical and Flow Zone Indictor (FZI) Methods for an Iraqi Gas Field

The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

Evaluation of reservoir characterization in the framework of electro-facies: a case study from the Bangestan reservoir in the Mansuri oilfield, SW Iran

Bangestan reservoir includes Sarvak and Ilam formations in the Mansouri oilfield consists mainly of carbonate rocks. In this research, we tried to overcome the problems in reservoir studies using well-logs data which are available in almost all wells in the oilfield. For this purpose, well-logs data from wells A and C were used as input data for the construction of electro-facies modeling. In the following, a model with 5 electro-facies was created and then this model was calibrated by microfacies in well A and core data such as porosity and permeability in the both wells in related to Sarvak Formation (wells A and C). The results of calibration reveal a good correlation between core data and electro-facies, so this model was propagated to un-cored wells in oilfield. The results of the study showed that reservoir quality from electro-facies 1 to 5 increases, respectively and pay zones of 2 in Ilam, 4 and 6 in Sarvak formations have the best reservoir quality. In order to future study, the velocity deviation log (VDL) that is mainly dependent on the type of dominant porosity in reservoir, was investigated to the specified electro-facies. Therefore, this log can be used as a tool for establishment of connection between porosity types and petrophysical data such as electro-facies.

Rock typing using geological and petrophysical data in the Asmari reservoir, Aghajari Oilfield, SW Iran

The Oligo-Miocene Asmari Formation, which extends over the entire Zagros foreland basin, is one of the most important hydrocarbon reservoirs in the Middle East. The Aghajari Field is one of the largest oilfields in the Zagros basin with the Asmari Formation being the major reservoir rock. We used petrophysical and geological data, by means of the neural network clustering technique, to identify rock types in the Asmari reservoir. Using petrophysical logs, such as gamma ray, sonic, density and neutron, along with calculated reservoir data (water saturation and effective porosity) from 20 wells (5 cored and 15 uncored wells) and their correlation with capillary pressure (Pc) curves, led to recognition of 5 electrofacies (EF1-EF5). These electrofacies were used to extend our interpretations to 60 uncored wells. The link between electrofacies and geological data (obtained from 1100m cores and 2500 thin sections) indicate that both sedimentary and diagenetic processes controlled the reservoir quality of the Asmari Formation. Porosity, permeability, water saturation, and reservoir quality index (RQI) were used to estimate reservoir quality of each electrofacies. EFs 1 and 2 with low porosity and permeability, high water saturation and low RQI are considered as non-reservoir that are related to sedimentary textures (argillaceous nature of wackestone-packstones) and effect of destructive diagenetic processes (cementation, physical and chemical compaction). EFs 3 and 4 show high values of porosity, permeability, RQI, and low percent of water saturations, which characterizes them as good reservoirs rocks. Vuggy, growth framework and interparticle porosities are major in EF-3, while the intercrystaline porosity is the major type in EF-4. Finally, EF-5 is the only electorfacies in the siliciclastic parts of the Asmari Reservoir, which is composed of rounded and well-sorted quartz grains that are slightly cemented. The latter is the best reservoir electrofacies in the Asmari Formation of the Aghajari oilfield due to its’ high values of porosity, permeability, and RQI and low percent of water saturation. Based on vertical distribution of EFs in the Asmari Formation, zones 1, 2, 3 and 4 are the best zones for drilling new productive wells around the oilfield. The results of this research can be useful in the development of the field, taking a decision and proper strategy in the connection of hydrocarbon production and recovery in the future of this field and similar fields.

Facies Architecture of the Fluvial-Aeolian Buchan Formation (Upper Devonian) and Its Implications on Field Exploration: A Case Study from Ardmore Field, Central North Sea, UK

The Upper Devonian Buchan Formation in the Central North Sea is a typical terrestrial deposit and predominantly comprises fine to medium-grained sandstones with occasional conglomerates and mudstones. The Buchan Formation has been previously described as being made up mostly of braided fluvial sandstones; however, this study confirms the presence and significance of aeolian sandstones within this fluvial-dominated sequence. Facies architecture is investigated through analogue outcrop study, well log curves and numerical facies modelling, and the results show contrasting differences between fluvial and aeolian facies. The fluvial facies is composed of multiple superimposed and sand-dominated fining-upward cycles in the vertical direction, and laterally an individual cycle has a large width/thickness ratio but is smaller than the field scale. However, the high channel deposition proportion (CDP, average value = 72%) in fluvial-dominated intervals means that it is likely all the sand bodies are interconnected. Aeolian facies comprise superimposed dune and interdune depositions and can be laterally correlated over considerable distances (over 1 km). Although the aeolian sandstones are volumetrically minor (approx. 30%) within the whole Buchan Formation, they have very high porosity and permeability (14.1% - 28%, 27 - 5290 mD) and therefore are excellent potential reservoirs. The fluvial sandstones are significantly cemented by quartz overgrowth and dolomite and by comparison with the aeolian sandstones are poor reservoirs. Aeolian sandstones can be differentiated from fluvial sandstones using several features: pin-stripe lamentation, good sorting, high visible porosity, friable nature and lack of muddy or conglomeratic contents; these characteristics allow aeolian sandstones can be tentatively recognized by low gamma ray values, high sonic transit time and low density in uncored wells. The thin, laterally correlatable and permeable aeolian sandstones within the Buchan Formation are effective reservoirs and could form important exploration targets when the Devonian is targeted elsewhere in the North Sea.

Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria

Most commonly, to estimate permeability, we can use values of porosity, pore size distribution, and water saturation from logging data and established correlations. One benefit of using wireline log data to estimate permeability is that it can provide a continuous permeability profile throughout a particular interval.This study will focus on the evaluation of formation permeability for a sandstone reservoir in the reservoir formations of Hassi R’Mel Field Southern from well log data using the multivariate methods. In order to improve the permeability estimation in these reservoirs, several statistical regression techniques have already been tested in previous work to correlate permeability with different well logs. It has been shown that statistical regression for data correlation is quite promising. We propose a two-step approach to permeability prediction that utilizes non-parametric regression in conjunction with multivariate statistical analysis. First we classify the well log data into electrofacies types. A combination of principal component analysis, model-based cluster analysis and discriminant analysis is used to characterize and identify electrofacies types. Second, we apply non-parametric regression techniques to predict permeability using well logs within each electrofacies. Three non-parametric approaches are examined via alternating conditional expectations (ACE), generalized additive model (GAM) and neural networks (NNET) and the relative advantages and disadvantages are explored. The results are compared with three other approaches to permeability predictions that utilize data partitioning based on reservoir layering, lithofacies information and hydraulic flow units. An examination of the error rates associated with discriminant analysis for uncored wells indicates that data classification based on electrofacies characterization is more robust compared to other approaches.These methods are tested and compared at the heterogeneous reservoirs in Triassic formations of Hassi R’Mel. The results show that permeability prediction is improved by applying variable selection to non-parametric regression ACE while tree regression is unable to predict permeability.In comparing the relative predictive performance of the three regression methods, the alternating conditional expectations with ACE method appears to outperform the other two methods.

Permeability Prediction for Nahr-Umr Reservoir / Subba field by Using FZI Method

The permeability determination in the reservoirs that are anisotropic and heterogeneous is a complicated problem due to the limited number of wells that contain core samples and well test data. This paper presents hydraulic flow units and flow zone indicator for predicting permeability of rock mass from core for Nahr-Umr reservoir/ Subba field. The Permeability measurement is better found in the laboratory work on the cored rock that taken from the formation. Nahr-Umr Formation is the main lower cretaceous sandstone reservoir in southern of Iraq. This formation is made up mainly of sandstone. Nahr-Umr formation was deposited on a gradually rising basin floor. The digenesis of Nahr-Umr sediments is very important due to its direct relation to the porosity and permeability.
 In this study permeability has been predicated by using the flow zone indicator methods. This method attempts to identify the flow zone indicator in un-cored wells using log records. Once the flow zone indicator is calculated from the core data, a relationship between this FZI value and the well logs can be obtained.
 Three relationships have been found for Nahr-Umr reservoir/Subba field by FZI method.
 By plotting the permeability of the core versus the permeability that is predicted by FZI method the parameter R2 was found (0.905) which is very good for predict the permeability.

Estimation of total organic carbon and brittleness volume

The Barnett Shale in the Fort Worth Basin is one of the most important resource plays in the USA. The total organic carbon (TOC) and brittleness can help to characterize a resource play to assist in the search for sweet spots. Higher TOC or organic content are generally associated with hydrocarbon storage and with rocks that are ductile in nature. However, brittle rocks are more amenable to fracturing with the fractures faces more resistant to proppant embedment. Productive intervals within a resource play should therefore contain a judicious mix of organics and mineralogy that lends to hydraulic fracturing. Identification of these intervals through core acquisition and laboratory-based petrophysical measurements can be accurate but expensive in comparison with wireline logging. We have estimated TOC from wireline logs using Passey’s method and attained a correlation of 60%. However, errors in the baseline interpretation can lead to inaccurate TOC. Using nonlinear regression with Passey’s TOC, normalized stratigraphic height, and acquired wireline logs, the correlation increased to 80%. This regression can be applied to uncored wells with logs to estimate TOC, and we used it as a ground truth in integrated analysis of seismic and well log data. The brittleness index (BI) is computed based on core Fourier transform infrared mineralogy using Wang and Gale’s formula. The correlation between core BI and estimated BI using elastic logs ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]) combined with wireline logs was 78%. However, this correlation decreases to 66% if the BI is estimated using only wireline logs. Therefore, the later serves as a less reliable proxy. We have correlated production to volumetric estimate of TOC and brittleness by computing distance-weighted averages in 120 horizontal wells. We have obtained a production correlation of 38% on blind wells, which was encouraging, suggesting that the geologic component in completions provides an important contribution to well success.

Flow prediction using well log data and intelligent techniques

ABSTRACTSpatial distribution of petrophysical properties within the reservoirs is important to provide a reliable reservoir model. The most complete reservoir description is generally provided through the identification of flow units. Then, the constructed model can be used for reservoir simulation of different production scenarios within the reservoir. However, before performing modeling and simulation jobs, accurate prediction of flow units is an important task and to reach a reliable petrophysical description first of all one should be able to predict flow units with high precision. Achieving this goal will be discussed in this article.This paper suggests intelligent techniques using artificial neural network (ANN), fuzzy logic (FL), and neuro-fuzzy (NF) to determine flow units from well log data. For this purpose, available routine core and wireline log data from five wells in Ilam Formation at the Ahwaz super giant oil field are used to find the best intelligent formulation between core-derived flow units and well log data. Validation of the predictive capability of the models was evaluated in one separate cored well (blind test well). The results show that FL-derived flow units had better accuracy with respect to other techniques. This comparison showed that FL can be used as the most reliable intelligent technique for flow unit prediction from well log data in uncored wells.

Permeability Prediction in One of Iraqi Carbonate Reservoir Using Hydraulic Flow Units and Neural Networks

Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.
 This paper will try to develop the permeability predictive model for one of Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).
 Histogram analysis, probability analysis and Log-Log plot of Reservoir Quality Index (RQI) versus normalized porosity (øz) are presented to identify optimal hydraulic flow units. Four HFUs were distinguished in this study area with good correlation coefficient for each HFU (R2=0.99), therefore permeability can be predicted from porosity accurately if rock type is known.
 Conventional core analysis and well log data were obtained in well 1 and 2 in one of carbonate Iraqi oil field. The relationship between core and well log data was determined by Artificial Neural Network (ANN) in cored wells to develop the predictive model and then was used to develop the flow units prediction to un-cored wells. Finally permeability can be calculated in each HFU using effective porosity and mean FZI in these HFUs. Validation of the models evaluated in a separate cored well (Blind-Test) which exists in the same formation. The results showed that permeability prediction from ANN and HFU matched well with the measured permeability from core data with R2 =0.94 and ARE= 1.04%.

The Continuous-Scratch Profile: A High-Resolution Strength Log for Geomechanical and Petrophysical Characterization of Rocks

The Continuous-Scratch Profile: A High-Resolution Strength Log for Geomechanical and Petrophysical Characterization of Rocks

Advances in grain-size, mineral, and pore-scale characterization of lithic and clay-rich reservoirs

Clay- and lithic-rich sandstones are difficult to characterize through uncored well sections in terms of their grain size, porosity, and mineralogy, all of which are required for assessing reservoir quality and production performance. This paper presents results from a study through one such interval and shows how a combination of different techniques can be used to better understand rock properties of complex reservoirs, thereby helping to reduce reservoir uncertainty. In this study, mean data from laser grain-size analysis are comparable to point-counted grain size, and both are considered as viable analytical methods. Automated quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN®) provides a further useful and consistent grain-size measurement that can be applied to both core and cuttings samples. The QEMSCAN has also proved to be a valuable technique in the mineralogical analysis of sandstones that are lithic, clay- and feldspar-rich, eliminating the subjective nature that is inherent with optical analysis. Results from the studied interval show that porosity measured by conventional core analysis (CA) and mercury injection capillary pressure (MICP) analysis are generally comparable with log-derived total porosity. Porosity measured from point-counting and QEMSCAN techniques is significantly lower than total porosity, with the QEMSCAN porosity locally equivalent to log-derived effective porosity. Both point-count and QEMSCAN porosities show better correlations with permeability ( and 0.94, respectively) than total porosity values ( and 0.60 CA and MICP, respectively), suggesting that they might provide a measure of effective porosity in high-quality reservoir rocks.

Genetic unit averages of pseudo-normalized pore throat radius for improved permeability predictions (Niger Delta as case study)

Recent studies in permeability modeling demonstrate a relationship between measured permeability and normalized pore throat radius $$ \overline{{R_{\text{tot}} }} $$ that can be used as a predictor over six (6) orders of magnitude in permeability for the clastic reservoirs using Niger Delta dataset. The practical significance of this relationship indicates that the response variable (core permeability) is strictly valid within the confidence interval: 0.005 ≤ $$ \overline{{R_{\text{tot}} }} $$ (µm) ≤ 10. The proposed model was validated extensively exhibiting an excellent correlation coefficient and statistical significance as depicted by their p and t tests. A major input parameter for the proposed $$ \overline{{R_{\text{tot}} }} $$ permeability model is the pore throat radius at any given pressure step (R pi ). This is an intrinsic component of capillary pressures obtained from mercury injection capillary pressures (MICP) test. The proposed model is hampered due to unavailability of MICP dataset for all wells drilled to generate the R pi parameter. This paper seeks to develop genetic unit averages of pseudo-normalized pore throat radius as input parameter to the $$ \overline{{R_{\text{tot}} }} $$ proposed model for improved permeability modeling. We also present an upscaling from core plug scale to well log scale–pseudo normalized pore throat radius for application in uncored well intervals for various depositional environments within the study area. The statistical significance of the coefficient of the proposed model depicts the sample statistic to be too unlikely to have occurred by chance, with their p values strictly below the α level of 0.05. The proposed model validation indicates an excellent adjusted correlation coefficient and lower errors based on analysis of variance (ANOVA) using approximately 1000 routine core and well log dataset from the four distinct environments of deposition.

Statistical verification of hydraulic units in a heterogeneous reservoir of the Liaohe Oilfield

The Liaohe Oil Field has passed peak production and correct discrimination of hy- draulic units (HU) has vital significance for forecasting remaining oil distribution in a petroleum reservoir, enhancing the recovery ratio and adjusting development plans. A unified multi-parameter cluster analysis and fuzzy quality synthetic evaluations have been used for the identification of res- ervoir hydraulic units. This paper analyzes three predictions within Block Shen-95: intersection of multiple well-logs, independent mulitple well-logs and mutually exclusive multiple well-logs. HU has been delineated to conveniently compute permeability and serve as the basis of a structural model for enhanced simulation study. HU has been defined by the flow zone indicator concept using a modified Kozeny-Carmen equation. The Bayesian method was used to predict HU at uncored wells by constructing a probability database and then integrating established HU and well-log responses at cored wells. HU has then been inferred from the database using well-log responses. Estimated permeability from predicted HU gave an overall improved permeability match when compared with traditional statistical methods. The method proved most favourable when using mutually exclusive multiple well-logs, most significant by integrating reservoir performance with HU distribution and indicating that reasonable prediction had been obtained at uncored wells using this mutually exclu- sive approach. The distribution pattern was revealed by interwell HU correlation using modified depositional cycles as a framework, an integration step that qualitatively examines prediction accu- racy. Detailed analysis has been carried out to determine and verify the characteristics of each kind of flow unit, providing a detailed geological basis for control of the oil field.

A modified fuzzy inference system for estimation of the static rock elastic properties: A case study from the Kangan and Dalan gas reservoirs, South Pars gas field, the Persian Gulf

Rock deformation parameters including Young's modulus, Poisson's ratio, shear modulus and bulk modulus (E, υ, K and G, respectively) are important geomechanical properties that have many applications in wellbore stability studies. In this study, rock mechanical properties were predicted from well log data by using an improved Fuzzy Inference System (FIS) in the Kangan and Dalan supergiant gas reservoirs, South Pars Gas Field. Using the results of laboratory tests on 33 core samples (static value) and formation wave velocity outcomes (dynamic value) empirical correlations were developed between them. Static rock deformation parameters were calculated using the empirical correlations in all logged intervals. In the first stage of estimating rock deformation modules in un-cored wells, four individual fuzzy models were generated. The correlation coefficients between predicted and calculated E, υ, K and G in the test data were 0.89, 0.49, 0.94 and 0.82, respectively. In an effort to achieve further prediction accuracy, a larger fuzzy model which can simultaneously estimate all parameters was generated. The new model is actually an integration of four previous models in which the outputs of each individual fuzzy model besides well log data are considered as its inputs.Using such an improved fuzzy model the correlation coefficients between predicted and calculated K, E, υ and G data in the testing samples increased to 0.941, 0.993, 0.983 and 0.999, respectively. The results of this study show that there is a good agreement between real and fuzzy-logic derived rock deformation parameters. Fuzzy logic is successful in estimation of static mechanical properties from well logs in un-cored wells where laboratory experiments and shear wave velocity data are missing.

Permeability Prediction in Carbonate Reservoir Rock Using FZI

Knowledge of permeability, which is the ability of rocks to transmit the fluid, is important for understanding the flow mechanisms in oil and gas reservoirs.Permeability is best measured in the laboratory on cored rock taken from the reservoir. Coring is expensive and time-consuming in comparison to the electronic survey techniques most commonly used to gain information about permeability.Yamama formation was chosen, to predict the permeability by using FZI method. Yamama Formation is the main lower cretaceous carbonate reservoir in southern of Iraq. This formation is made up mainly of limestone. Yamama formation was deposited on a gradually rising basin floor. The digenesis of Yamama sediments is very important due to its direct relation to the porosity and permeability.In this study permeability has been predicated by using the Flow zone indicator methods.This method attempts to identify the flow zone indicator in un-cored wells using log records. Once the flow zone indicator is calculated from the core data, a relationship between this FZI value and the well logs can be obtained.

Porosity and Permeability Prediction Based on Computational Intelligences as Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in Southern Carbonate Reservoir of Iran

Reservoir characterization is a hard-to-do task because of the extremely heterogeneous nature of petroleum bearing formations. Studying different sources of data obtained from underground formations shows that abrupt changes in reservoir rock properties are very commonplace, especially in carbonate formations. Overcoming heterogeneity of reservoir is seemed to be impossible at least with current practices. In addition, obtaining reliable data from every foot for all wells is not feasible because of its high cost as well as being very time-consuming. Porosity and permeability distribution are essential reservoir rock properties to be determined in order to build a reservoir model with acceptable accuracy. Analyzing well test and core data are two reliable sources of porosity and permeability determination. Due to the additional time and cost, coring from all points of formation is not feasible. Therefore another way of defining porosity and permeability distribution should be sought in which a more available source of data is used. The fact that geophysical well logs are routinely run for every wells makes researcher to find a way to predict porosity and permeability for uncored wells by correlating well logs and core data. Computational intelligence are intelligent approach that be used to estimate permeability and porosity in cored zone and related the results to uncored zone. Fuzzy logic, genetic algorithm, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are some kind of computational intelligence. The authors used ANN and ANFIS as two models to estimate of permeability and porosity in Southern Carbonate reservoir of Iran and results compared with each other to have good decision about methods of computational intelligence.

Robust committee machine for water saturation prediction

Water saturation is one of the important physical properties of the petroleum reservoir which are usually determined by core analysis. An accurate determination of this parameter is significant to execute a realistic evaluation of hydrocarbon reserves in the formation and also decreasing the economic risk. In this study, a robust technique is proposed to determine an accurate value of this parameter from well log data in un-cored well or at un-cored interval of the same well by combining different types of machine learning techniques. The final results (sub-CM outputs) demonstrated that integrating these techniques using proposed method provides an accurate, fast and cost-effective method for estimating the target value.

Sedimentology and diagenesis as ‘hydrocarbon exploration tools’ in the Late Permian Zechstein-2 Carbonate Member (NE Netherlands)

Abstract Hydrocarbon exploration in The Netherlands has a chequered history from serendipitous oil shows via chance oil/ gas discoveries to finding the largest continental European oil field in 1943, followed by finding the largest gas field in the world in 1959. The present contribution traces the development of moderate to good porosity/permeability trends in depositional facies of Zechstein Stassfurt carbonates in a ‘gas play’ intermediate in significance between the above two plays but all in the northern part of The Netherlands. Various depositional facies in the Stassfurt carbonates were turned into ‘carbonate fabric units’ by diagenetic processes creating or occluding the porosity/permeability. This formed moderate to good gas reservoirs in barrier-shoal, open-marine shelf and proximal-slope carbonates in the subsurface of the province of Drenthe in the NE Netherlands. The diagenetic models forming these carbonate fabric units are linked to the variety of facies in a depositional model which shows explain and predicts the reservoir trends. Such depositional/diagenetic facies are ‘translated’ into characteristic petrophysical values recognisable on wire line logs in uncored wells, and in characteristic seismic expressions that show these trends in undrilled areas. This approach has been proven to be effective in delineating porosity trends, visualised by 3-D seismic in the Collendoornerveen field, and thus provides a new exploration ‘tool’ in hydrocarbon exploration .

가스하이드레이트 퇴적층 물성 추정 소프트웨어를 이용한 울릉분지 시추공 자료 해석

미래의 청정 에너지자원인 가스하이드레이트 개발을 위해 국내 부존이 유망한 울릉분지 5개의 지역에 대하여 2007년 시추작업을 수행하여 모든 시추공으로부터 물리검층 자료를 취득하였으며 이중 UBGH1-04, UBGH1-09, UBGH1-10 시추공에서 코어 자료를 취득하였다. 이 연구에서는 기확립한 가스하이드레이트 퇴적층 물성 추정 기법 및 UBGH1-04, UBGH1-09, UBGH1-10 시추공에서의 물성 추정 결과를 바탕으로 사용자 친화적 소프트웨어인 "KMU GH Logs 2010"을 개발하였다. 또한 코어 미회수 시추공인 UBGH1-01 및 UBGH1-14 시추공의 물리검층 자료를 이용하여 가스하이드레이트 퇴적층의 물성을 추정하였다. 밀도 검층 자료를 사용하여 공극률을 추정하였으며, 전기비저항 검층 및 음파 검층을 이용하여 가스하이드레이트포화율을 추정하였다. 물리검층 자료와 코어의 퇴적상 분석 자료를 이용하여 선형 판별 분석 기법을 통해 퇴적상을 추정함으로써 가스하이드레이트 해리의 징후가 나타나는 DITM 및 MSS 퇴적상에 대한 판별이 가능함을 확인하였다. For the development of gas hydrate as new future energy resources, the drilling was carried out at the five locations where have high potential as gas hydrate bearing sediments in Ulleung basin, offshore Korea in 2007. Well log data were obtained from all wells and core data were procured from 3 wells, UBGH1-04, UBGH1-09 and UBGH1-10. In this study, user-friendly software, "KMU GH Logs 2010", is developed and this software is based on the estimation methods developed in previous study for gas hydrate bearing sediments and the properties estimated from UBGH1-04, UBGH1-09 and UBGH1-10. Petrophysical properties in un-cored wells, UBGH1-01 and UBGH1-14, are also estimated by using well log data. Porosity is estimated by density log and gas hydrate saturation is calculated by sonic log and resistivity log. Sedimentary facies are estimated by applying the linear discriminant analysis using both well log and sedimentary facies data from core analysis. It is confirmed that DITM facies and MSS facies appeared signs of gas hydrate disassociation are able to be distinguished by the method.