Integrated 3D Seismic, Petrophysical Data of the Sarvak Formation, Sirri District in the Persian Gulf

Bashari, A.

doi:10.3997/2214-4609-pdb.24.p01

Cited by 4 publications

(5 citation statements)

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“…Sarvak is deposited in three different depositional environments. Upper Sarvak belongs to fore reef with good reservoir quality; middle part belongs to main reef, which has the best reservoir quality, consequently very good productivity; and bottom Sarvak is a back reefal facies, related to low-energy environment with bad reservoir quality (Bashari 2007a). This sequence is comparable to the final results of this work too.…”

Section: Discussionmentioning

confidence: 99%

“…Sarvak formation (Upper Albian to Upper Turonian) is an important carbonate reservoir of Bangestan Group, and stratigraphically is equivalent to Mauddud, Khatiyah and Mishrif sequences in the Persian Gulf (Bashari 2007a). Simplified stratigraphy of Cretaceous is presented in figure 2.…”

Section: Geological Settingmentioning

confidence: 99%

“…In 2007, by integrating seismic and well log data, a 3D static model of the Mishrif reservoir (upper Sarvak) was created in Sirri district, the Persian Gulf. According to the static model, it was concluded that a gradual transition exists between Khatiyeh (a basinal deposition facies, equivalent to Q3 middle Sarvak) and Mishrif facies but the transition zone varies in thickness (Bashari 2007b). Bashari has completed another Q4 investigation about the Mishrif reservoir in the Reshadat oil field, the Persian Gulf.…”

Section: Introductionmentioning

confidence: 99%

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Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

Masoudi

Tokhmechi

Bashari³

et al. 2012

Journal of Geophysics and Engineering

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Sarvak formation is the second major carbonate reservoir in Iran. There are several geological, petrophysical and geophysical investigations which have been carried out on this important reservoir. In this work, Sarvak is studied to find productive zones. At first, four different methods were used to identify producing intervals from well log data and well test results. Then, final zoning is generated by integrating outputs of these four methods. One of them is the conventional cutoff based method; the other three methods are based on flow equation, Bayesian and fuzzy theories. Thereafter, by considering the classification correctness rate of each classifier in each well and technique of majority voting, a unique zoning for Sarvak formation is presented. Based on the final zoning, the whole Sarvak interval is divided into seven zones. Three of them are classified as oil producing zones, two of them cannot be classified as conventionally producing zones, and the remaining two are water producing. Zone number 2 not only has the highest production rate, but also is the most homogeneous zone among the productive zones. The novelty of this research is using well test results in defining productive classes, which improves the certainty of classification in comparison with previous works that were based on core analysis and log data.

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Section: Discussionmentioning

confidence: 99%

Section: Geological Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

Masoudi

Tokhmechi

Bashari³

et al. 2012

Journal of Geophysics and Engineering

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“…Utilized data in both datasets consists of calculated porosity, shale content and water saturation, in addition to well test results for verification. (Bashari, 2007;Mahbaz et al, 2011). As it is reported from the studied field (we render this field as "S" field due to studying a sandy reservoir through), Burgan Member is overlaid on unnamed clastics, after a sedimentation gap.…”

Section: Datasets and Geologic Settingsmentioning

confidence: 97%

Net pay determination by Dempster rule of combination: Case study on Iranian offshore oil fields

Masoudi

Arbab

Mohammadrezaei

2014

Journal of Petroleum Science and Engineering

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International audienceNet pay detection is a key stage in reservoir characterization for several purposes: reserve estimation, reservoir modeling and simulation, production planning, etc. Determining productive zones always is simultaneous with some amount of uncertainty due to lack of enough data, insufficiency of knowledge and wild-nature of petroleum reservoirs. It becomes even more challenging in carbonates, because of their highly heterogeneous environment. Conventionally, evaluating net pays is done by applying petrophysical cutoffs on well-logs, which results in crisp classification of pay or non-pay zones. In addition, cutoff based method is developed in sandstones, and does not provide suitable results in carbonates at all. Proposed methodology of this work, Dempster-Shafer Theory, is a generalization of Bayesian Theory of conditional probabilities. Net pays are studied in two oil reservoirs by this theory; one of them is carbonate reservoir of Mishrif, the other is sandy Burgan reservoir. For validation, results 2 are compared to well tests and output of conventional cutoff method. The advantages of using Dempster-Shafer Theory, comparing to conventional cutoff based method in studying net pays is: to have a continuous fuzzy output, based on geological facts, with high generalization ability and more compatibility with well test data

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“…Second dataset, belongs to Sandy Burgan reservoir with the age of Middle Cretaceous (Albian era). Burgan is a Member of Kazhdumi Formation (Bashari, 2007;Mahbaz et al, 2011). Based on IOOC reports in the field of which Burgan is investigation (we call it Second Field here), Burgan Member is deposited on unnamed clastics, after a sedimentation gap.…”

Section: Datasets and Geologic Settingsmentioning

confidence: 99%

Net pay determination by artificial neural network: Case study on Iranian offshore oil fields

Masoudi¹,

Arbab²,

Mohammadrezaei³

2014

Journal of Petroleum Science and Engineering

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International audienceDetermining productive zones has always been a challenge for petrophysicists. On the other hand, Artificial Neural Networks are powerful tools in solving identification problems. In this paper, pay zone determination is defined as an identification problem, and is tried to solve it by trained Neural Networks. Proposed methodology is applied on two datasets: one belongs to carbonate reservoir of Mishrif, the other belongs to sandy Burgan reservoir. The results showed high precision in classifying productive zones in predefined classes with Classification Correctness Rate of more than 85% in both geological conditions. Applicability of proposed pay zone determination procedure in carbonate environment is a great advantage of developed methodology. Fuzzified output, being independent of core tests and verification with well tests results are of other advantages of Neural Network-based method of pay zone detection

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Integrated 3D Seismic, Petrophysical Data of the Sarvak Formation, Sirri District in the Persian Gulf

Cited by 4 publications

References 0 publications

Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods

Net pay determination by Dempster rule of combination: Case study on Iranian offshore oil fields

Net pay determination by artificial neural network: Case study on Iranian offshore oil fields

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