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>

Reservoir Inhomogeneities of Some Recent Sand Bodies

1972 ◽  
Vol 12 (03) ◽  
pp. 229-245 ◽  
Author(s):  
Wayne A. Pryor
Keyword(s):  
Grain Size ◽  
Depositional Environments ◽  
Beach Sand ◽  
Textural Characteristics ◽  
Dune Sands ◽  
Reservoir Characteristics ◽  
Beach Sands ◽  
Sandstone Reservoirs ◽  
Grain Sorting ◽  
Sand Bodies

Abstract Sandstone reservoirs are the results of long and frequently complex histories of geologic evolution. The combined processes of deposition, burial compaction diagenesis and structural deformation yield final reservoir bodies of widely varying geometries, permeability-porosity characteristics, and structural configurations that are difficult to predict. In unraveling the evolution of sandstone predict. In unraveling the evolution of sandstone reservoirs, it is necessary to have detailed knowledge of their initial depositional characteristics and of the post-depositional modifications impressed upon them. This knowledge can provide a rational basis in predicting the characteristics of reservoir bodies away from areas of data control. To present, little information pertaining to the reservoir characteristics of freshly deposited sand bodies bas been available. In an API-sponsored study, permeabilities, porosities, and textural properties were derived from 992 oriented and properties were derived from 992 oriented and undisturbed sand samples of river bars, beaches and dunes undergoing active sedimentation. River point-bar samples have permeabilities ranging from 4 md to more than 500 darcies and average 93 darcies. Porosities in the river point bars range from 17 to 52 percent and average 41 percent. Beach sand samples have a permeability percent. Beach sand samples have a permeability range of 3.6 to 166 darcies and average 68 darcies. Porosities in beach sands range from 39 to 56 Porosities in beach sands range from 39 to 56 percent and average 49 Percent. Permeability values percent and average 49 Percent. Permeability values in dune sands range from 5 to 104 darcies and average 54 darcies. Dune-sand porosities range from 42 to 55 percent and average 49 percent. Permeabilities in river-bar sands are extremely Permeabilities in river-bar sands are extremely variable compared with those of beaches and dunes. In river bars, permeability decreases systematically downstream and bankward. Although of low variability, permeabilities on beaches are low on the beach faces, high on the beach crests, and variable on the beach berm areas. Both river-bar and beach sands have well organized directional permeabilities, parallel to the length of the bodies permeabilities, parallel to the length of the bodies in river bars and perpendicular to the length of the bodies in beaches. Dunes are characterized by low variability in permeability and porosity and show no significant patterns or trends. There is greater variability within bedding and lamination packets than between them. In addition, the boundary conditions between bedding and lamination packets are important factors in determining the effective reservoir characteristics of sand bodies, to the extent that a bedding unit of higher permeability completely surrounded by units of lower permeability will not demonstrate its ultimate through-flow capabilities, but will have an effective permeability influenced by and largely determined by the lower permeabilities of the bounding units. River-bar sand bodies have a significantly different arrangement and variability between bedding units than do beaches or dunes. The ideal relationships between permeability-porosity and textural characteristics permeability-porosity and textural characteristics that various authors have set forth for artificially packed particles are only weakly demonstrated by packed particles are only weakly demonstrated by these natural sands from various depositional environments. In all three depositional environments, permeability increases with increase in grain permeability increases with increase in grain size and porosity increases with increase in grain sorting. However, in river-bar sands permeability increases as grain sorting increases and porosity increases as grain size increases, just the opposite of the relationships in beach-dune sands and in the artificially packed grain experiments. The underlying cause of these deviations is the different style of grain packing in the river-bar sands. Introduction Permeability and porosity are important characteristics of sand reservoir bodies; their magnitudes, patterns, and variabilities significantly influence the migration, accumulation, and distribution of fluids and gases in the reservoirs, and just as significantly determine the ability of reservoirs to release their fluids and gases to production stimulation. SPEJ P. 229

scholarly journals Sedimentology and Ichnology of Paleozoic Estuarine and Shoreface Reservoirs, Morrow Sandstone, Lower Pennsylvanian of Southwest Kansas, USA

1999 ◽  
pp. 1-35
Author(s):  
Luis A. Buatois ◽  
Gabriela M. Mangano ◽  
Timothy R. Carr
Keyword(s):  
Oil And Gas ◽  
Petroleum Industry ◽  
Integrated Approach ◽  
Trace Fossils ◽  
Depositional Environments ◽  
Trace Fossil ◽  
Reservoir Modeling ◽  
Delta Front ◽  
Valley Fill ◽  
Biogenic Structures

Integration of facies and trace-fossil evidence tests and refines depositional models constructed solely on the basis of physical sedimentology. In recent years, the petroleum industry has increasingly used trace-fossil analysis of cores as an aid in reservoir characterization. In particular, ichnologic data have been instrumental in the recognition of estuarine deposits and their distinction from open-marine facies (e.g., MacEachern and Pemberton, 1994). Previous ichnologic analyses of cores, however, have concentrated on post-Paleozoic reservoirs (e.g., Bockelie, 1991; Pemberton, 1992; Taylor and Gawthorpe, 1993; Howell et al., 1996; Martin and Pollard, 1996; MacEachern and Pemberton, 1997). The present study represents one of the first attempts to apply trace-fossil analysis to cores from Paleozoic reservoirs. The Lower Pennsylvanian Morrow Sandstone contains oil and gas reservoirs in a wide variety of shallow and marginal-marine depositional environments. Delta-front, shoreface, and estuarine valley-fill reservoir sandstones are encased in offshore and estuarine mudstones (Sonnenberg, 1985; Krystinik and Blakeney, 1990; Sonnenberg et al., 1990; Wheeler et al., 1990). An integrated stratigraphic, sedimentologic, and ichnologic study provides a more accurate characterization of reservoir facies and geometry. This study allows distinction between marine-shoreface and estuarine valley-fill sandstones from four cores of the lower Morrow in southwestern Kansas. Core analysis subsequently was integrated with well-log information. Previous studies have emphasized the presence of estuarine valley-fills in the upper Morrow (Wheeler et al., 1990). Our integrated approach extends the estuarine valley interpretation into the lower Morrow. Within the midcontinent, trace fossils are useful in distinguishing different facies in estuarine incised valleys and marine shorefaces. Detailed study of biogenic structures provides high-resolution information to solve problems in facies, stratigraphic, and reservoir modeling. In some cases, they represent the only evidence available to develop a reasonable picture of depositional conditions and to estimate reservoir heterogeneity. The present study provides a detailed analysis of the sedimentary facies, documents the associated trace fossils, and illustrates how trace fossils are used to refine environmental interpretations of the lower Morrow sandstone reservoirs.

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 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. 

scholarly journals Geological Model for Khasib Formation of East Baghdad Field Southern Area

2020 ◽  
Vol 10 (3) ◽  
pp. 21-35
Author(s):  
Layth A. Jameel ◽  
Fadhil S. Kadhim ◽  
Hussein Ilaibi Al-Sudani
Keyword(s):  
Production Capacity ◽  
Volumetric Method ◽  
Oil Field ◽  
Sequential Gaussian Simulation ◽  
Geological Model ◽  
Petrophysical Properties ◽  
Middle Region ◽  
Oil Reserves ◽  
Southern Area ◽  
Gaussian Simulation

Geological model construction is an important phase of reservoir study as the production capacity of a reservoir depends on its structural and petrophysical characteristics. The economic benefit of the reservoir is evaluated by estimating the formation petrophysical properties and calculating the oil reserves. East Baghdad southern area field is a newly developing oil field in the middle region of Iraq, where Khasib formation is its main reservoir. The aim of this study is to estimate the petrophysical properties and determine the pay units of the formation under study and the initial oil in place. Sequential Gaussian Simulation was used here to distribute the petrophysical properties as the statistical method and volumetric method was used to calculate the oil in place. The results show that the main reservoir units of the formation are K2 and K3 units, and the estimated oil reserves equal to 2179 mmSTB (346.43 million cubic meters).

scholarly journals Understanding reservoir heterogeneity using variography and data analysis: an example from coastal swamp deposits, Niger Delta Basin (Nigeria)

Geologos ◽  
2020 ◽  
Vol 26 (3) ◽  
pp. 207-218
Author(s):  
Ifeanyichukwu S. Obi ◽  
K. Mosto Onuoha ◽  
Olusegun T. Obilaja ◽  
C. I. Princeton Dim
Keyword(s):  
Data Analysis ◽  
Depositional Environments ◽  
Sequential Gaussian Simulation ◽  
Sediment Provenance ◽  
Reservoir Prediction ◽  
Clear Trend ◽  
Field Development ◽  
Net To Gross ◽  
Gaussian Simulation ◽  
Niger Delta Basin

Abstract For efficient reservoir management and long-term field development strategies, most geologists and asset managers pay special attention to reservoir chance of success. To minimise this uncertainty, a good understanding of reservoir presence and adequacy is required for better ranking of infill opportunities and optimal well placement. This can be quite challenging due to insufficient data and complexities that are typically associated with areas with compounded tectonostratigraphic framework. For the present paper, data analysis and variography were used firstly to examine possible geological factors that determine directions in which reservoirs show minimum heterogeneity for both discrete and continuous properties; secondly, to determine the maximum range and degree of variability of key reservoir petro-physical properties from the variogram, and thirdly, to highlight possible geological controls on reservoir distribution trends as well as areas with optimal reservoir quality. Discrete properties evaluated were lithology and genetic units, while continuous properties examined were porosity and net-to-gross (NtG). From the variogram analysis, the sandy lithology shows minimum heterogeneity in east-west (E–W) and north-south (N–S) directions, for Upper Shoreface Sands (USF) and Fluvial/Tidal Channel Sands (FCX/TCS), respectively. Porosity and NtG both show the least heterogeneity in the E–W axis for reservoirs belonging to both Upper Shoreface and Fluvial Channel environments with porosity showing a slightly higher range than NtG. The vertical ranges for both continuous properties did not show a clear trend. The Sequential Indicator Simulation (SIS) and Object modelling algorithm were used for modelling the discrete properties, while Sequential Gaussian Simulation (SGS) was used for modelling of the continuous properties. Results from this exercise show that depositional environment, sediment provenance, topographical slope, sub-regional structural trends, shoreline orientation and longshore currents, could have significant impacts on reservoir spatial distribution and property trends. This understanding could be applied in reservoir prediction and for generating stochastic estimates of petrophysical properties for nearby exploration assets of similar depositional environments.

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