scholarly journals Static reservoir modeling using stochastic method: a case study of the cretaceous sequence of Gamtoos Basin, Offshore, South Africa

2021 ◽  
Author(s):  
O. L. Ayodele ◽  
T. K. Chatterjee ◽  
M. Opuwari
Keyword(s):  
South Africa ◽  
Water Saturation ◽  
3D Models ◽  
Sequential Gaussian Simulation ◽  
Reservoir Modeling ◽  
Northwestern Part ◽  
Analysis Model ◽  
Reservoir Properties ◽  
Depositional Sequence ◽  
Gaussian Simulation

AbstractGamtoos Basin is an echelon sub-basin under the Outeniqua offshore Basin of South Africa. It is a complex rift-type basin with both onshore and offshore components and consists of relatively simple half-grabens bounded by a major fault to the northeast. This study is mainly focused on the evaluation of the reservoir heterogeneity of the Valanginian depositional sequence. The prime objective of this work is to generate a 3D static reservoir model for a better understanding of the spatial distribution of discrete and continuous reservoir properties (porosity, permeability, and water saturation). The methodology adopted in this work includes the integration of 2D seismic and well-log data. These data were used to construct 3D models of lithofacies, porosity, permeability, and water saturation through petrophysical analysis, upscaling, Sequential Indicator Simulation, and Sequential Gaussian Simulation algorithms, respectively. Results indicated that static reservoir modeling adequately captured reservoir geometry and spatial properties distribution. In this study, the static geocellular model delineates lithology into three facies: sandstone, silt, and shale. Petrophysical models were integrated with facies within the reservoir to identify the best location that has the potential to produce hydrocarbon. The statistical analysis model revealed sandstone is the best facies and that the porosity, permeability, and water saturation ranges between 8 and 22%, 0.1 mD (< 1.0 mD) to 1.0 mD, and 30–55%. Geocellular model results showed that the northwestern part of the Gamtoos Basin has the best petrophysical properties, followed by the central part of the Basin. Findings from this study have provided the information needed for further gas exploration, appraisal, and development programs in the Gamtoos Basin.

scholarly journals Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

2019 ◽  
Vol 10 (2) ◽  
pp. 569-585 ◽  
Author(s):  
Ebong D. Ebong ◽  
Anthony E. Akpan ◽  
Stephen E. Ekwok
Keyword(s):  
Spatial Variability ◽  
Niger Delta ◽  
Water Saturation ◽  
Stochastic Modelling ◽  
Sequential Gaussian Simulation ◽  
Stochastic Methods ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Petrophysical Property ◽  
Niger Delta Basin

Abstract Three-dimensional models of petrophysical properties were constructed using stochastic methods to reduce ambiguities associated with estimates for which data is limited to well locations alone. The aim of this study is to define accurate and efficient petrophysical property models that best characterize reservoirs in the Niger Delta Basin at well locations and predicting their spatial continuities elsewhere within the field. Seismic data and well log data were employed in this study. Petrophysical properties estimated for both reservoirs range between 0.15 and 0.35 for porosity, 0.27 and 0.30 for water saturation, and 0.10 and 0.25 for shale volume. Variogram modelling and calculations were performed to guide the distribution of petrophysical properties outside wells, hence, extending their spatial variability in all directions. Transformation of pillar grids of reservoir properties using sequential Gaussian simulation with collocated cokriging algorithm yielded equiprobable petrophysical models. Uncertainties in petrophysical property predictions were performed and visualized based on three realizations generated for each property. The results obtained show reliable approximations of the geological continuity of petrophysical property estimates over the entire geospace.

scholarly journals Penentuan OOIP Berdasarkan Pemodelan Geologi dan Reservoir di Daerah Tanisha Cekungan Sumatra Selatan

2018 ◽  
Vol 2 (2) ◽  
pp. 14
Author(s):  
Nila Rahayu ◽  
Ratnayu Sitaresmi ◽  
Moeh. Ali Jambak
Keyword(s):  
Depositional Environments ◽  
Reservoir Rock ◽  
Sequential Gaussian Simulation ◽  
Reservoir Modeling ◽  
Geological Model ◽  
Delta Front ◽  
Petrophysical Analysis ◽  
Reservoir Characteristics ◽  
Sandstone Reservoirs ◽  
Gaussian Simulation

<p>Perkembangan teknologi dapat dimanfaatkan untuk mengetahui karakteristik reservoir sebelum dilakukannya kegiatan eksplorasi dan eksploitasi. Salah satunya dengan pemodelan geologi dan pemodelan reservoir untuk mendapatkan gambaran bentuk bawah permukaan, karakteristik reservoir, dan <em>OOIP</em>.  Analisis data log dan interpretasi geologi dilakukan untuk mendapatkan informasi lingkungan pengendapan, marker lapisan, dan bentukan struktur reservoir yang digunakan sebagai dasar pembuatan model geologi. Analisis petrofisik akan memberikan informasi mengenai karakteristik batuan reservoir. Untuk mendapatkan model reservoir, hasil analisis petrofisik akan didistribusikan pada model geologi. Kemudian penentuan <em>OOIP</em> dapat dihitung dengan menggunakan metode volumetrik. Reservoir batupasir sudah terbukti menjadi reservoir produktif di berbagai lapangan migas, seperti reservoir batupasir pada Formasi Talang Akar di Lapangan Sungai Lilin. Terdapat enam lapisan yang menjadi obyek penelitian pada Formasi Talang Akar yaitu lapisan D1, D2, E1, E2, F, dan H yang diendapkan pada lingkungan delta plain–delta front terlihat dari pola log yang berkembang yaitu <em>funnel shape, serrated shape</em>, dan <em>bell shape</em>. Perbedaan lingkungan pengendapan akan mempengaruhi geometri dan karakteristik reservoir. Didapatkan nilai <em>cut-off</em> untuk Vcl ≤0.40, porositas ≥0.10 dan saturasi air ≤0.7. Hasil analisis petrofisika kemudian didistribusikan pada model geologi dengan metode <em>Sequential Gaussian Simulation</em> , dimana penyebaran lingkungan pengendapan menjadi arahan dasar penyebaran properti reservoir. Perhitungan <em>OOIP</em> pada enam lapisan di Formasi Talang Akar berdasarkan pemodelan reservoir sebesar 8,387 MSTB, dengan lapisan menarik terdapat pada lapisan E2 2,340 MSTB. </p><p><em>Technological developments can be utilized to determine reservoir characteristics prior to exploration and exploitation activities. One of them is by geological modeling and reservoir modeling to get a picture of subsurface shapes, reservoir characteristics, and OOIP. Log data analysis and geological interpretation were carried out to obtain information on depositional environments, layer markers, and reservoir structure formations that were used as the basis for making geological models. Petrophysical analysis will provide information about reservoir rock characteristics. To get the reservoir model, the results of the petrophysical analysis will be distributed to the geological model. Then the determination of OOIP can be calculated using the volumetric method. </em><em>Sandstone reservoirs have proven to be productive reservoirs in various oil and gas fields, such as sandstone reservoirs in the Talang Akar Formation in Sungai Lilin Field. There are six layers that are the object of research in the Talang Root Formation, namely layers D1, D2, E1, E2, F, and H which are deposited in the plain-delta front delta environment as seen from the developing log pattern, namely funnel shape, serrated shape, and bell shape. The difference in depositional environments will affect the geometry and characteristics of the reservoir. Obtained cut-off values for Vcl ≤0.40, porosity ≥0.10 and water saturation ≤0.7. The results of the petrophysical analysis are then distributed to the geological model using the Sequential Gaussian Simulation method, where the spread of the depositional environment is the basis for spreading reservoir properties. The OOIP calculation for the six layers in the Talang Akar Formation is based on reservoir modeling of 8,387 MSTB, with an interesting layer found at the E2 layer 2,340 MSTB.</em></p>

scholarly journals Stochastic reservoir modelling for prospect mapping: a case study of ''bright'' field, Niger delta

2020 ◽  
Vol 8 (1) ◽  
pp. 102
Author(s):  
James Sunday Abe ◽  
Mary Taiwo Olowokere ◽  
Pius Adekunle Enikanselu
Keyword(s):  
Water Saturation ◽  
Stochastic Modelling ◽  
Total Porosity ◽  
Effective Porosity ◽  
Sequential Gaussian Simulation ◽  
Reservoir Modeling ◽  
Reservoir Modelling ◽  
Petrophysical Property ◽  
Geostatistical Approach ◽  
Net To Gross

Deterministic reservoir modeling using geostatistical approach is inherently ambiguous because of the uncertainties contained in the generated reservoir models. Stochastic reservoir modelling using sequential gaussian simulation algorithm can resolve this problem by generating various realizations of petrophysical property models in order to map this uncertainties caused by subsurface heterogeneity. Suites of well logs for four wells with seismic data in SEG-Y format were used for this analysis. The wells were correlated and a reservoir was mapped across them in other to map their lateral extent, synthetic seismogram was generated in other to match the event on the seismic with that of the synthetic after carrying out a shift of -12ms. Seismic to well tie was done to ensure that the horizons were mapped accurately. The structural maps generated and the wells were input that goes into the stochastic modelling process. Five realizations each of facies(lithology), effective porosity, total porosity, net to gross, volume of shale and one realization for permeability and water saturation were generated. The facies models showed the distribution of sand and shale with sand at the existing well locations and the effective porosity, total porosity, net to gross, volume of shale models showed excellent values around the well location. Permeability and water saturation models showed that the existing wells were drilled at the flank of the anticlinal structure. Two drillable points (prospects) were proposed by considering all the initial petrophysical property models and the parameters of the two points named P1 and P2 showed that they contain hydrocarbon in commercial quantity. Stochastic reservoir modelling has proved effective in mapping uncertainties and detecting bypassed hydrocarbons.  

scholarly journals A Practical Method to Calculate and Model the Petrophysical Properties of Reservoir Rock Using Petrel Software: A case Study from Iraq

2020 ◽  
pp. 2640-2650
Author(s):  
Sarah Taboor Wali ◽  
Hussain Ali Baqer
Keyword(s):  
Water Saturation ◽  
Three Dimensional ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Sequential Gaussian Simulation ◽  
Geological Model ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Software Models ◽  
Average Water

Nasiriyah oilfield is located in the southern part of Iraq. It represents one of the promising oilfields. Mishrif Formation is considered as the main oil-bearing carbonate reservoir in Nasiriyah oilfield, containing heavy oil (API 25o(. The study aimed to calculate and model the petrophysical properties and build a three dimensional geological model for Mishrif Formation, thus estimating the oil reserve accurately and detecting the optimum locations for hydrocarbon production. Fourteen vertical oil wells were adopted for constructing the structural and petrophysical models. The available well logs data, including density, neutron, sonic, gamma ray, self-potential, caliper and resistivity logs were used to calculate the petrophysical properties. The interpretations and environmental corrections of these logs were performed by applying Techlog 2015 software. According to the petrophysical properties analysis, Mishrif Formation was divided into five units (Mishrif Top, MA, shale bed, MB1 and MB2).    A three-dimensional geological model, which represents an entrance for the simulation process to predict reservoir behavior under different hydrocarbon recovery scenarios, was carried out by employing Petrel 2016 software. Models for reservoir characteristics (porosity, permeability, net to gross NTG and water saturation) were created using the algorithm of Sequential Gaussian Simulation (SGS), while the variogram analysis was utilized as an aid to distribute petrophysical properties among the wells.      The process showed that the main reservoir unit of Mishrif Formation is MB1 with a high average porosity of 20.88% and a low average water saturation of 16.9%. MB2 unit has good reservoir properties characterized by a high average water saturation of 96.25%, while MA was interpreted as a water-bearing unit. The impermeable shale bed unit is intercalated between MA and MB1 units with a thickness of 5-18 m, whereas Mishrif top was interpreted as a cap unit. The study outcomes demonstrated that the distribution accuracy of the petrophysical properties has a significant impact on the constructed geological model which provided a better understanding of the study area’s geological construction. Thus, the estimated reserve h was calculated to be about 7945 MSTB. This can support future reservoir development plans and performance predictions. 

New Insights in the Evaluation of Reserves of Selected wells of the Pletmos Basin Offshore South Africa

2016 ◽  
Author(s):  
Samir Elamri ◽  
Mimonitu Opuwari
Keyword(s):  
South Africa ◽  
Water Saturation ◽  
Base Case ◽  
Reservoir Properties ◽  
Vertical Heterogeneity ◽  
Structural Configurations ◽  
Shaly Sand ◽  
Core Description ◽  
Hydrocarbon Reserves ◽  
High Uncertainty

ABSTRACT The area evaluated has similar structural styles and settings as the producing neighboring fields of F-A and E-M in the adjacent Bredasdorp basin Offshore South Africa. The main objective of this study is to create a 3-D-static model and estimate hydrocarbon reserves. Based on log signatures, petrophysical properties and structural configurations, the reservoirs were divided vertically into three reservoir units in order to be properly modelled in 3-D space. The thicknesses of the layers were determined based on the vertical heterogeneity in the reservoir properties. Facies interpretation was performed based on log signatures, core description and previous geological studies. The volume of clay and porosity was used to classify facies into five units of sand, shaly sand, silt, and clay. From petrophysical interpretation, a synthetic permeability log was generated in the wells which ties closely with core data. The J-function water saturation model was adopted because it produced better results in the clean sandstone sections of the reservoirs. A high uncertainty in the basement formation was observed due to very few wells drilled in the area and fault impact and thus resulted in evaluation of uncertainty of each zone separately. The uncertainty workflow was run using 100 trials and the base case P50 estimated 277 Bcf of Gas from the 1At1.

Research on Characteristics and Three-Dimension Geologic Model Building of the Chang2 Reservoir, Yanchang Formation in Zhifang Area, Zhidan Oilfield

2012 ◽  
Vol 466-467 ◽  
pp. 287-292
Author(s):  
Kun Sheng Qiang ◽  
Jian Min Wang ◽  
Xin Wen Tian ◽  
Xiu Xiang Lü ◽  
Li Wang
Keyword(s):  
Model Building ◽  
Simulation Method ◽  
Sequential Gaussian Simulation ◽  
Low Permeability ◽  
Physical Analysis ◽  
Three Dimension ◽  
Reservoir Properties ◽  
Thin Sections ◽  
Field Development ◽  
Gaussian Simulation

Through the reservoir of Chang 2 member in Zhifang area, Zhidan oilfield is typical of low porosity and low permeability reservoir rocks, using casting thin sections, scanning electron microscopy and physical analysis, etc., starting with the reservoir characteristics of Chang2 member, we have understood its causes and characteristics of low permeability. Using sequential indicator simulation and based on the target body method, we established the head of the construction of the reservoir model in Chang 2 member, using the sequential Gaussian simulation method combined with phase control principle, a model of reservoir properties in this area was established, Getting a high quality reservoir and field development and deployment of later development and further adjustments to tap the potential pratical guidance.

scholarly journals Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

2021 ◽  
Author(s):  
Ayodele O. Falade ◽  
John O. Amigun ◽  
Yousif M. Makeen ◽  
Olatunbosun O. Kafisanwo
Keyword(s):  
Niger Delta ◽  
Structural Model ◽  
Water Saturation ◽  
Sequential Gaussian Simulation ◽  
Surface Generation ◽  
Geostatistical Modeling ◽  
Reservoir Properties ◽  
Field Development ◽  
Net To Gross ◽  
Well Data

AbstractThis research aims at characterizing and modeling delineated reservoirs in ‘Falad’ Field, Niger Delta, Nigeria, to mitigate the challenge caused by the heterogeneous nature of the reservoirs. Seismic and well log data were integrated, and geostatistics was applied to describe the reservoir properties of the interwell spaces within the study area. Four reservoirs, namely RES 1, RES 2, RES 3 and RES 4, were delineated and correlated across four wells. The reservoir properties {lithology, net to gross, porosity, permeability, water saturation} of all the delineated reservoirs mapped were determined, and two reservoirs with the best quality were picked for further analysis (surface generation and modeling) after ranking the reservoirs based on their quality. Structural interpretation of the field was carried, nine faults were mapped (F1—F9), and the fault polygon was generated. The structural model showed the area is structurally controlled with two of the major faults mapped (F1 and F3) oriented in the SW–NE direction while the other one (F4) is oriented in the NW–SE direction. A 3D grid was constructed using the surfaces of the delineated reservoirs and the reservoir properties were distributed stochastically using simple krigging method with sequential Gaussian simulation, sequential indicator simulation and Gaussian random function simulation algorithms. Geostatistical modeling used in this study has been able to give subsurface information in the areas deficient of well data as the estimated reservoir properties gotten from existing wells have been spatially distributed in the study area and will thus aid future field development while also they are used in identifying new prospect by combining property models with structural maps of the area.

Duration of water saturation in selected soils of Weatherley, South Africa

2007 ◽  
Vol 24 (3) ◽  
pp. 152-160 ◽  
Author(s):  
C. W. van Huyssteen ◽  
P. A.L. le Roux ◽  
M. Hensley ◽  
T. B. Zere
Keyword(s):  
South Africa ◽  
Water Saturation

scholarly journals Microvessel Chaste: An Open Library for Spatial Modelling of Vascularized Tissues

2017 ◽  
Author(s):  
J.A. Grogan ◽  
A.J. Connor ◽  
B. Markelc ◽  
R.J. Muschel ◽  
P.K. Maini ◽  
...  
Keyword(s):  
Open Source ◽  
Tumour Growth ◽  
Spatial Models ◽  
3D Models ◽  
Tissue Growth ◽  
Coronary Perfusion ◽  
Analysis Model ◽  
User Friendly

AbstractSpatial models of vascularized tissues are widely used in computational physiology, to study for example, tumour growth, angiogenesis, osteogenesis, coronary perfusion and oxygen delivery. Composition of such models is time-consuming, with many researchers writing custom software for this purpose. Recent advances in imaging have produced detailed three-dimensional (3D) datasets of vascularized tissues at the scale of individual cells. To fully exploit such data there is an increasing need for software that allows user-friendly composition of efficient, 3D models of vascularized tissue growth, and comparison of predictions with in vivo or in vitro experiments and other models. Microvessel Chaste is a new open-source library for building spatial models of vascularized tissue growth. It can be used to simulate vessel growth and adaptation in response to mechanical and chemical stimuli, intra- and extra-vascular transport of nutrient, growth factor and drugs, and cell proliferation in complex 3D geometries. The library provides a comprehensive Python interface to solvers implemented in C++, allowing user-friendly model composition, and integration with experimental data. Such integration is facilitated by interoperability with a growing collection of scientific Python software for image processing, statistical analysis, model annotation and visualization. The library is available under an open-source Berkeley Software Distribution (BSD) licence at https://jmsgrogan.github.io/MicrovesselChaste. This article links to two reproducible example problems, showing how the library can be used to model tumour growth and angiogenesis with realistic vessel networks.

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