scholarly journals Petrophysical Core-Based Zonation of An Oil Field In The Bredasdorp Basin South Africa

Author(s):  
Mimonitu Opuwari ◽  
Blessing Afolanyan ◽  
Saeed Mohammed ◽  
Paschal Ogechukwu Amaechi ◽  
Y Bareja ◽  
...  

Abstract This study aims to generate rock units based on core permeability and porosity of an oil field in the Bredasdorp Basin offshore South Africa. In this study, we identified and classified lithofacies based on sedimentology reports in conjunction with well logs. Lucia's petrophysical classification method is used to classify rocks into three classes. Results revealed three lithofacies as A(sandstone, coarse to medium-grained), B (fine to medium-grained sandstone), and C (carbonaceous claystone, finely laminated with siltstone). Lithofacies A is the best reservoir quality and corresponds to class 1, while lithofacies B and C correspond to class 2 and 3, which are good and poor reservoir quality rock, respectively. An integrated reservoir zonation for the rocks is based on four different zonation methods (Flow Zone indicator (FZI), Winland r35, Hydraulic conductivity (HC), and Stratigraphy modified Lorenz plot (SMLP)). Four flow zones were identified as high(HFZ), moderate (MFZ), Low (LFZ), and tight (TFZ), respectively. The HFZ is the best reservoir quality composed of a megaporous rock unit, with an average FZI value between 5 to 10µm, and HC from 40 to 120 mD/v3, ranked as very good. The most prolific flow units (HFZ and MFZ zones) form more than 75 % of each well's flow capacities. The TFZ is the most reduced rock quality composed of impervious and nanoporous rock. There appears to be a slight increase of illite in the tight and low zones that block pore throats, thereby decreasing permeability. Therefore, illite has a dominant effect on flow zones. Quartz is the dominant framework grain, and siderite is the dominant cement that affects flow zones. This study has demonstrated a robust approach to delineate flow units in an oilfield. A novel sandstone reservoir zonation classification criteria developed from this study can be applied to other datasets of sandstone reservoirs with confidence.

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Mimonitu Opuwari ◽  
Blessing Afolayan ◽  
Saeed Mohammed ◽  
Paschal Ogechukwu Amaechi ◽  
Youmssi Bareja ◽  
...  

AbstractThis study aims to generate rock units based on core permeability and porosity of OW oilfield in the Bredasdorp Basin offshore South Africa. In this study, we identified and classified lithofacies based on sedimentology reports in conjunction with well logs. Lucia's petrophysical classification method is used to classify rocks into three classes. Results revealed three lithofacies as A (sandstone, coarse to medium-grained), B (fine to medium-grained sandstone), and C (carbonaceous claystone, finely laminated with siltstone). Lithofacies A is the best reservoir quality and corresponds to class 1, while lithofacies B and C correspond to class 2 and 3, which are good and poor reservoir quality rock, respectively. An integrated reservoir zonation for the rocks is based on four different zonation methods (Flow Zone indicator (FZI), Winland r35, Hydraulic conductivity (HC), and Stratigraphy modified Lorenz plot (SMLP)). Four flow zones Reservoir rock types (RRTs) were identified as RRT1, RRT3, RRT4, and RRT5, respectively. The RRT5 is the best reservoir quality composed of a megaporous rock unit, with an average FZI value between 5 and 10 µm, and HC from 40 to 120 mD/v3, ranked as very good. The most prolific flow units (RRT5 and RRT4 zones) form more than 75% of each well's flow capacities are supplied by two flow units (FU1 and FU3). The RRT1 is the most reduced rock quality composed of impervious and nanoporous rock. Quartz is the dominant framework grain, and siderite is the dominant cement that affects flow zones. This study has demonstrated a robust approach to delineate flow units in the OW oilfield. We have developed a useful regional petrophysical reservoir rock flow zonation model for clastic reservoir sediments. This study has produced, for the first time, insights into the petrophysical properties of the OW oilfield from the Bredasdorp Basin South Africa, based on integration of core and mineralogy data. A novel sandstone reservoir zonation classification criteria developed from this study can be applied to other datasets of sandstone reservoirs with confidence.


Author(s):  
Ahmed E. Radwan ◽  
Bassem S. Nabawy ◽  
Ahmed A. Kassem ◽  
Walid S. Hussein

AbstractWaterflooding is one of the most common secondary recovery methods in the oil and gas industry. Globally, this process sometimes suffers a technical failure and inefficiency. Therefore, a better understanding of geology, reservoir characteristics, rock typing and discrimination, hydraulic flow units, and production data is essential to analyze reasons and mechanisms of water injection failure in the injection wells. Water injection failure was reported in the Middle Miocene Hammam Faraun reservoir at El Morgan oil field in the Gulf of Suez, where two wells have been selected as injector’s wells. In the first well (A1), the efficiency of injection was not good, whereas in the other analog A2 well good efficiency was assigned. Therefore, it is required to assess the injection loss in the low efficiency well, where all aspects of the geological, reservoir and production data of the studied wells were integrated to get a complete vision for the reasons of injection failure. The available data include core analysis data (vertical and horizontal permeabilities, helium porosity, bulk density, and water and oil saturations), petrographical studies injection and reservoir water chemistry, reservoir geology, production, and injection history. The quality of the data was examined and a set of reliable X–Y plots between the available data were introduced and the reservoir quality in both wells was estimated using reservoir quality index, normalized porosity index, and flow zone indicator. Integration and processing of the core and reservoir engineering data indicate that heterogeneity of the studied sequence was the main reason for the waterflooding inefficiency at the El Morgan A1 well. The best reservoir quality was assigned to the topmost part of the reservoir, which caused disturbance of the flow regime of reservoir fluids. Therefore, it is clearly indicated that rock typing and inadequate injection perforation strategy that has not been aligned with accurate hydraulic flow units are the key control parameters in the waterflooding efficiency.


Author(s):  
Masoud Soleimani ◽  
◽  
Bahman Soleimani ◽  
Bahram Alizadeh ◽  
Iman Veisy ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Libing Fu ◽  
Jun Ni ◽  
Yuming Liu ◽  
Xuanran Li ◽  
Anzhu Xu

Abstract The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.


2017 ◽  
Vol 21 (4) ◽  
pp. 565-577 ◽  
Author(s):  
Farshad Bahrami ◽  
Reza Moussavi-Harami ◽  
Mohammad Khanehbad ◽  
Mohamad Hosein Mahmudy Gharaie ◽  
Rahmatollah Sadeghi

2002 ◽  
Vol 5 (02) ◽  
pp. 135-145 ◽  
Author(s):  
G.R. King ◽  
W. David ◽  
T. Tokar ◽  
W. Pape ◽  
S.K. Newton ◽  
...  

Summary This paper discusses the integration of dynamic reservoir data at the flow-unit scale into the reservoir management and reservoir simulation efforts of the Takula field. The Takula field is currently the most prolific oil field in the Republic of Angola. Introduction The Takula field is the largest producing oil field in the Republic of Angola in terms of cumulative oil production. It is situated in the Block 0 Concession of the Angolan province of Cabinda. It is located approximately 25 miles offshore in water depths ranging from 170 to 215 ft. The field consists of seven stacked, Cretaceous reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This paper discusses the data acquisition and integration for this reservoir only. The reservoir was discovered in January 1980 with Well 57- 02X. Primary production from the reservoir began in December 1982. The reservoir was placed on a peripheral waterflood in December 1990. Currently, the Upper Vermelha reservoir accounts for approximately 75% of the production from the field. Sound management of mature waterfloods has been identified as a key to maximizing the ultimate recovery and delivering the highest value from the Block 0 Asset.1 Therefore, the objective of the simulation effort was to develop a tool for strategic and dayto- day reservoir management with the intent of managing and optimizing production on a flow-unit basis. Typical day-to-day management activities include designing workovers, identifying new well locations, optimizing injection well profiles, and optimizing sweep efficiencies. To perform these activities, decisions must be made at the scale of the individual flow units. In general, fine-grid geostatistical models are developed from static data, such as openhole log data and core data. Recent developments in reservoir characterization have allowed for the incorporation of some dynamic data, such as pressure-transient data and 4D seismic data, into the geostatistical models. Unfortunately, pressure-transient data are acquired at a test-interval scale (there are typically 3 to 4 test intervals per well, depending on the ability to isolate different zones mechanically in the wellbore), while seismic data are acquired at the reservoir scale. The reservoir surveillance program in the Takula field routinely acquires data at the flow-unit scale. These data include openhole log and wireline pressure data from newly drilled wells and casedhole log and production log (PLT) data from producing/injecting wells. Because of the time-lapse nature of cased-hole log and PLT data, they represent dynamic reservoir data at the flow-unit scale. To achieve the objectives of the modeling effort and optimize production on a flow-unit basis, these dynamic data must be incorporated into the simulation model at the appropriate scale. When these data are incorporated into a simulation model, it is typically done during the history match. There are, however, instances when these data are incorporated during other phases of the study. The objective of this paper, therefore, is to discuss the methods used to integrate the dynamic reservoir data acquired at the flow-unit scale into the Upper Vermelha reservoir simulation model. Reservoir Geology The geology of the Takula field is described in detail in Ref. 2. The aspects of the reservoir geology that are pertinent to this paper are elaborated in this section. Reservoir Stratigraphy. The Takula field consists of seven stacked reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This reservoir contains an undersaturated, 33°API crude oil. For reservoir management purposes, 36 marker surfaces have been identified in the reservoir. Flow units were then identified as reservoir units separated by areally pervasive vertical flow barriers (nonreservoir rock). This resulted in the identification of 20 flow units. The thickness of these flow units ranges from 5 to 15 ft. Reservoir Structure. The reservoir structure is a faulted anticline that is interpreted to be the result of regional salt tectonics. Closure to the reservoir is provided by faults on the southwestern and northern flanks of the structure and by an oil/water contact (OWC) on the eastern, western, and southern flanks of the structure. A structure map of the reservoir is presented in Fig. 1. Data Acquisition in the Takula Field Openhole Log Program. Most original development wells were logged with a basic log suite of resistivity/gamma ray and density/ neutron logs. In addition, the vertical wells drilled from each well jacket were logged with a sonic log and, occasionally, velocity surveys. All wells drilled after 1993 were logged with long spacing sonic and spectral gamma ray logs. In many wells drilled after December 1997, carbon/oxygen (C/O) logs have been run in open hole to distinguish between formation and injected water.3 A few recent wells have been logged with nuclear magnetic resonance (NMR) logs. The NMR log data, when integrated with data from other logs, have been of value in distinguishing free water from bound water, formation water from injection water, and reservoir rock from nonreservoir rock.


2020 ◽  
Vol 8 (1) ◽  
pp. SA25-SA33
Author(s):  
Ellen Xiaoxia Xu ◽  
Yu Jin ◽  
Sarah Coyle ◽  
Dileep Tiwary ◽  
Henry Posamentier ◽  
...  

Seismic amplitude has played a critical role in the exploration and exploitation of hydrocarbon in West Africa. Class 3 and 2 amplitude variation with offset (AVO) was extensively used as a direct hydrocarbon indicator and reservoir prediction tool in Neogene assets. As exploration advanced to deeper targets with class 1 AVO seismic character, the usage of seismic amplitude for reservoir presence and quality prediction became challenged. To overcome this obstacle, (1) we used seismic geomorphology to infer reservoir presence and precisely target geophysical analysis on reservoir prone intervals, (2) we applied rigorous prestack data preparation to ensure the accuracy and precision of AVO simultaneous inversion for reservoir quality prediction, and (3) we used lateral statistic method to sum up AVO behavior in regions of contrasts to infer reservoir quality changes. We have evaluated a case study in which the use of the above three techniques resulted in confident prediction of reservoir presence and quality. Our results reduced the uncertainty around the biggest risk element in reservoir among the source, charge, and trap mechanism in the prospecting area. This work ultimately made a significant contribution toward a confident resource booking.


1984 ◽  
Vol 24 (1) ◽  
pp. 66 ◽  
Author(s):  
John D. Gorter

The quantity of organic matter in the source beds within the Horn Valley Siltstone, as defined by the Total Organic Carbon content, increases westward from low values in the south and east of the Basin to maximum values in the Mt Winter and Mereenie areas. This westerly enrichment trend is paralleled by an improvement in source rock quality, as defined by the Hydrogen Index and Tmax crossplot of samples analysed by Rock-Eval pyrolysis.Earlier attempts to measure thermal maturation levels of source rocks in the Basin relied on the reflectivity of coalified graptolites but this method was only applicable to unweathered material obtained from the few and scattered bore holes in the Basin. In this study, conodont colour alteration is used to define organic maturation levels. This technique, newly applied in Australia, was used principally on samples collected from the Horn Valley Siltstone and has the practical advantage of being applicable to samples from both outcrop and subsurface localities.The study indicates that the conodont colour alteration isograds in the Amadeus Basin are primarily related to events of the Alice Springs Orogeny, when the thick mass of molasse sediments (Pertnjara Group) resulting from erosion of the uplifted Arunta Block was deposited. Anomalies in the conodont colour isograds are closely related to timing of structural growth during the orogeny and also possibly to the growth of salt structures.In addition, the study shows that burial at depths below 1500 m will have led to the catagenetic breakdown of reservoired oil and the production of only gaseous hydrocarbons from source beds.In combination, these two factors lead to the conclusion that the most prospective area for oil sourced by the Horn Valley Siltstone is north and west of the Mereenie Oil Field in areas of shallow burial.


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