scholarly journals Development of HFU-based permeability prediction models using core data for characterisation of a heterogeneous Oligocene sand in the Nam Con Son basin

2021 ◽  
Vol 10 ◽  
pp. 33-39
Author(s):  
Văn Hiếu Nguyễn ◽  
Hồng Minh Nguyễn ◽  
Ngọc Quốc Phan ◽  
Huy Giao Phạm
Keyword(s):  
Grain Size ◽  
Prediction Models ◽  
Core Analysis ◽  
Permeability Model ◽  
Core Data ◽  
Distribution Index ◽  
Porosity And Permeability ◽  
Grain Sorting ◽  
Mercury Injection Capillary Pressure ◽  
Special Core Analysis

Core data by both routine and special core analysis are required to understand and predict reservoir petrophysical characteristics. In this research, a total number of 50 core plugs taken from an Oligocene sand (T30) in the Nam Con Son basin, offshore southern Vietnam, were tested in the core laboratory of the Vietnam Petroleum Institute (VPI). The results of routine core analysis (RCA) including porosity and permeability measurements were employed to divide the study reservoir into hydraulic flow units (HFUs) using the global hydraulic elements (GHEs) approach. Based on five classified HFUs, 16 samples were selected for special core analysis, i.e., mercury injection capillary pressure (MICP) and grain size analyses for establishing non-linear porosity-permeability model of each HFU based on Kozeny-Carman equation, which provides an improved prediction of permeability (R2 = 0.846) comparing to that by the empirical poro-perm relationship (R2 = 0.633). In addition, another permeability model, namely the Winland R35 method, was applied and gave very satisfactory results (R2 = 0.919). Finally, by integrating the results from MICP and grain size analyses, a good trendline of pore size distribution index (λ) and grain sorting was successfully obtained to help characterise the study reservoir. High λ came with poor sorting, and vice versa, the low λ corresponded to good sorting of grain size.

scholarly journals Reservoir Parameters Estimation with Some Log Curves of Qiongdongnan Basin Based on LS-SVM Method

2021 ◽  
Vol 2092 (1) ◽  
pp. 012024
Author(s):  
Tangwei Liu ◽  
Hehua Xu ◽  
Xiaobin Shi ◽  
Xuelin Qiu ◽  
Zhen Sun
Keyword(s):  
Prediction Models ◽  
Parameters Estimation ◽  
Qiongdongnan Basin ◽  
Support Vector ◽  
Well Log ◽  
Statistical Regression ◽  
Log Data ◽  
Core Data ◽  
Permeability Prediction ◽  
Porosity And Permeability

Abstract Reservoir porosity and permeability are considered as very important parameters in characterizing oil and gas reservoirs. Traditional methods for porosity and permeability prediction are well log and core data analysis to get some regression empirical formulas. However, because of strong non-linear relationship between well log data and core data such as porosity and permeability, usual statistical regression methods are not completely able to provide meaningful estimate results. It is very difficult to measure fine scale porosity and permeability parameters of the reservoir. In this paper, the least square support vector machine (LS-SVM) method is applied to the parameters estimation with well log and core data of Qiongdongnan basin reservoirs. With the log and core exploration data of Qiongdongnan basin, the approach and prediction models of porosity and permeability are constructed and applied. There are several type of log data for the determination of porosity and permeability. These parameters are related with the selected log data. However, a precise analysis and determine of parameters require a combinatorial selection method for different type data. Some curves such as RHOB,CALI,POTA,THOR,GR are selected from all obtained logging curves of a Qiongdongnan basin well to predict porosity. At last we give some permeability prediction results based on LS-SVM method. High precision practice results illustrate the efficiency of LS-SVM method for practical reservoir parameter estimation problems.

USING HIGH FIDELITY CONTINUOUS CORE DATA FOR A FAST AND OBJECTIVE ESTIMATION OF RESERVOIR QUALITY

2021 ◽  
Author(s):  
Izral Izarruddin Marzuki ◽  
◽  
Thanapala Murugesu ◽  
Christophe Germay ◽  
Tanguy Lhomme ◽  
...  
Keyword(s):  
Grain Size ◽  
High Resolution ◽  
Gamma Ray ◽  
High Volume ◽  
Quality Data ◽  
Reservoir Quality ◽  
Core Analysis ◽  
Test Procedures ◽  
Dna Test ◽  
Core Data

With this paper, we demonstrate how Core DNA, a trans-disciplinary suite of high-resolution, non-destructive measurements performed on whole cores at the onset of core analysis programs, helps operation geologists and petrophysicists with an innovative, cost effective and objective way to characterize the reservoir quality of highly laminated hydrocarbon-bearing formations where the standard practice (systematic plugging every foot) fails to provide a correct estimate. The case study focuses on core data from three wells intersecting formations characterized by very thin (millimetre-scale) sand and clay/silt laminations where the resolution of conventional wireline and lab gamma ray logs were not sufficiently sharp for an effective evaluation of reservoir quality. Although a high volume of routine core analysis data was already available for these wells, the remaining uncertainty on reservoir evaluation was deemed high enough by the study team to motivate the acquisition of additional data comprising ultra-high resolution pictures (1.8μm/px) and topographic maps created from micron-accurate laser scans. We explain how continuous profiles of grain size indicators could be used for the prediction of permeability variations across these laminated formations and for the definition of a permeability cut-off for the identification of poor vs good reservoir ratios compatible with the reservoir characteristics. Core DNA test procedures are specifically designed to greatly accelerate the deliverables of core analysis, so that petrophysical evaluation may start right from the moment cores arrive from the well site, which is usually month before routine core analysis results are known. In the context of this paper, Core DNA results were confirmed a-posteriori by the permeability measured on plugs samples from the two first wells. In the third well however, some marked differences were observed: although permeability ranges were found similar by the two methods, the distribution of permeability values obtained from routine core analysis conducted according to standard guidelines (one sample per foot) gave a more optimistic picture of permeability (90% rock above the 1mD cut-off) than the alternative approach based on high resolution continuous grain size data (70% rock above the 1mD cut-off). From the above findings, we conclude that a standard 1-ft interval for plug acquisition is not enough to fully characterise the distribution of permeability in highlyl aminated formations. Alternatively, a continuous profile of permeability index based on high resolution grain size measurements offers a fast and cost-efficient solution to obtain representative reservoir quality data, which enable objective well and reservoir management decisions few days after barrel opening without compromising core integrity for further studies.

Rekonstruksi dan Validasi Data Permeabilitas Relatif Untuk Proses History Matching Dalam Simulasi Reservoir Pengembangan Lapangan X

2015 ◽  
Vol 4 (2) ◽  
pp. 44-52
Author(s):  
Novia Rita ◽  
Tomi Erfando
Keyword(s):  
History Matching ◽  
Core Analysis ◽  
Black Oil ◽  
Special Core Analysis

Sebelum suatu model reservoir digunakan, terlebih dahulu dilakukan history matching atau menyesuaikan kondisi model dengan dengan kondisi reservoir. Salah satu parameter yang perlu disesuaikan adalah permeabilitas relatif. Untuk melakukan rekonstruksi nilai permeabilitas relatifnya dibutuhkan data SCAL (Special Core Analysis) dari sampel core. Langkah awal rekonstruksi adalah dengan melakukan normalisasi data permeabilitas relatif (kr) dan saturasi air (Sw) dari data SCAL yang berasal dari tiga sampel core. Setelah dilakukan nomalisasi, dilakukan denormalisasi data permeabilitas relatif yang akan dikelompokkan berdasarkan jenis batuannya. Setelah dilakukan history matching menggunakan black oil simulator, data denormalisasi tersebut belum sesuai dengan kondisi aktual. Selanjutnya digunakan persamaan Corey untuk rekonstruksi kurva permeabilitas relatifnya. Hasil dari persamaan tersebut didapat bahwa nilai kro dan krw jenis batuan 1 sebesar 0,25 dan 0,09 kemudian nilai kro dan krw untuk jenis batuan 2 sebesar 0,4 dan 0,2. Nilai permeabilitas dari persamaan Corey digunakan untuk melakukan history matching, hasilnya didapat kecocokan (matching) dengan keadaan aktual. Berdasarkan hasil simulasi, nilai produksi minyak aktualnya adalah 1.465.650 bbl sedangkan produksi dari simulasi adalah 1.499.000 bbl. Artinya persentase perbandingan aktual dan simulasinya adalah 1,14% yang dapat dikatakan cocok karena persentase perbedaannya di bawah 5%.

scholarly journals A new framework for selection of representative samples for special core analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 210-226 ◽  
Author(s):  
Abouzar Mirzaei-Paiaman ◽  
Seyed Reza Asadolahpour ◽  
Hadi Saboorian-Jooybari ◽  
Zhangxin Chen ◽  
Mehdi Ostadhassan
Keyword(s):  
Core Analysis ◽  
Representative Samples ◽  
Special Core Analysis ◽  
New Framework ◽  
Selection Of

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

scholarly journals Multifractal Characteristics and Classification of Tight Sandstone Reservoirs: A Case Study from the Triassic Yanchang Formation, Ordos Basin, China

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2242 ◽  
Author(s):  
Zhihao Jiang ◽  
Zhiqiang Mao ◽  
Yujiang Shi ◽  
Daxing Wang
Keyword(s):  
Pore Structure ◽  
Ordos Basin ◽  
Type I ◽  
Tight Sandstone ◽  
Throat Radius ◽  
Yanchang Formation ◽  
Porosity And Permeability ◽  
Sandstone Reservoir ◽  
And Migration ◽  
Mercury Injection Capillary Pressure

Pore structure determines the ability of fluid storage and migration in rocks, expressed as porosity and permeability in the macroscopic aspects, and the pore throat radius in the microcosmic aspects. However, complex pore structure and strong heterogeneity make the accurate description of the tight sandstone reservoir of the Triassic Yanchang Formation, Ordos Basin, China still a problem. In this paper, mercury injection capillary pressure (MICP) parameters were applied to characterize the heterogeneity of pore structure, and three types of pore structure were divided, from high to low quality and defined as Type I, Type II and Type III, separately. Then, the multifractal analysis based on the MICP data was conducted to investigate the heterogeneity of the tight sandstone reservoir. The relationships among physical properties, MICP parameters and a series of multifractal parameters have been detailed analyzed. The results showed that four multifractal parameters, singularity exponent parameter (αmin), generalized dimension parameter (Dmax), information dimension (D1), and correlation dimension (D2) were in good correlations with the porosity and permeability, which can well characterize the pore structure and reservoir heterogeneity of the study area, while the others didn’t respond well. Meanwhile, there also were good relationships between these multifractal and MICP parameters.

The Uniqueness of Heterogeneous Globigerina Limestone Reservoir and The Importance of Integrated Rock Type Definition for Field Development Plan: Case Study in Madura Strait, East Java

2021 ◽  
Author(s):  
E. P. Putra
Keyword(s):  
Water Saturation ◽  
Rock Type ◽  
Reservoir Rock ◽  
Reservoir Quality ◽  
Secondary Porosity ◽  
Field Development ◽  
Core Data ◽  
Type Definition ◽  
Volume Porosity ◽  
Porosity And Permeability

The Globigerina Limestone (GL) is the main reservoir of the seven gas fields that will be developed in the Madura Strait Block. The GL is a heterogeneous and unique clastic carbonate. However, the understanding of reservoir rock type of this reservoir are quite limited. Rock type definition in heterogeneous GL is very important aspect for reservoir modeling and will influences field development strategy. Rock type analysis in this study is using integration of core data, wireline logs and formation test data. Rock type determination applies porosity and permeability relationship approach from core data, which related to pore size distribution, lithofacies, and diagenesis. The analysis resulted eight rock types in the Globigerina Limestone reservoir. Result suggests that rock type definition is strongly influenced by lithofacies, which is dominated by packstone and wackestone - packstone. The diagenetic process in the deep burial environment causes decreasing of reservoir quality. Then the diagenesis process turns to be shallower in marine phreatic zone and causes dissolution which increasing the reservoir quality. Moreover, the analysis of rock type properties consist of clay volume, porosity, permeability, and water saturation. The good quality of a rock type will have the higher the porosity and permeability. The dominant rock type in this study area is RT4, which is identical to packstone lithofasies that has 0.40 v/v porosity and 5.2 mD as average permeability. The packstone litofacies could be found in RT 5, 6, 7, even 8 due to the increased of secondary porosity. It could also be found at a lower RT which is caused by intensive cementation.

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