NivaarExam PrepOfficial exam papers ↗

18-Geol-A3 Sedimentation and Stratigraphy · December 2018

Question 8 of 19: Importance of Grain-Size Data — Petroleum, Groundwater and Process Interpretation

Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)

Notes on this paper

EGBC National Exam — Geological Engineering, 18-Geol-A3, Sedimentation & Stratigraphy, 2018-Dec. Closed book, no calculator, 3 hours. Part 1: Sedimentology (Questions 1–12, 5 marks each) instructs "Answer questions 1 and 2, and any other 6 questions from the remaining 10 questions (i.e., questions 3–12)" for 40 marks total. Part 2: Stratigraphy and Sedimentary Basin Analysis (Questions 13–19, 5 marks each) instructs "Answer five of the following seven questions" for 25 marks total (65/65 maximum).

Reference texts: Nichols, Sedimentology and Stratigraphy, 2nd ed. (depositional environments, facies models, carbonate platforms, sequence stratigraphy, biostratigraphy and correlation); Boggs, Petrology of Sedimentary Rocks, 2nd ed. (sandstone and carbonate classification, weathering, diagenesis, grain-size analysis); Bjorlykke, Petroleum Geoscience: From Sedimentary Environments to Rock Physics, 2nd ed. (basin classification, reservoir facies).

Question 8: Importance of Grain-Size Data — Petroleum, Groundwater and Process Interpretation (5 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

(i) Petroleum production: grain size (and its associated sorting) is a first-order control on porosity and permeability — well-sorted, coarser sand has larger, better-connected pore throats and higher permeability, directly governing reservoir producibility, while poorly-sorted or fine-grained intervals form lower-quality reservoir or seal/baffle layers; grain-size logs are used to map reservoir-quality trends and predict flow-unit boundaries across a field. (ii) Groundwater studies: grain size (via empirical relations such as the Hazen approximation, K ∝ d10²) is used to estimate hydraulic conductivity of an unconsolidated aquifer directly from a sieve/grain-size analysis, informing well-yield prediction, contaminant-transport modelling and aquifer-vulnerability assessment without requiring an expensive pumping test. (iii) Depositional-process interpretation: grain-size distribution shape (unimodal vs. polymodal, degree of sorting, presence of a coarse tail) diagnoses the transport mechanism — e.g. a well-sorted unimodal fine sand implies aeolian or beach transport, a poorly-sorted polymodal distribution implies a debris flow or glacial deposit, and probability plots of grain-size data can separate the traction, saltation and suspension sub-populations within a single sample, reconstructing the flow's competence and mode of transport.