The course aims at providing the fundamentals needed to investigate, characterize, model and develop a hydrocarbon reservoir. The course integrates the knowledge gained during the previous courses (especially Geophysical Prospecting, Reservoir Geology, Petroleum Geomechanics and Fluid Mechanics in Porous Media) with the analytical and numerical approaches typical of reservoir engineering so as to obtain a consistent understanding of the reservoir dynamic behavior and to define field development strategies. Strict connections with the Well Drilling and Completion course, with the design of surface facilities (course on Petroleum Chemistry and Technology) and with economic considerations (course on Resources and Environmental Economics) are necessary when forecasting the future field performance and evaluating the effectiveness of the potential production strategies. For these reasons the reservoir engineering course is very central and has a key role in the petroleum engineering program, just like reservoir engineering would have in the industrial practice.
Specific goals of the course are to provide the ability of quality assessing and interpreting ' when needed - all the input data necessary to understand the reservoir behavior and to build a reservoir dynamic model, to understand the cause-effect relationships when history matching the reservoir past performance, and to optimize the field development and production strategy. The skills gained in the Reservoir Engineering course will allow the students to cooperate at, or to be in charge of, integrated reservoir studies. Students will acquire the competences needed to efficiently communicate with experts from other disciplines who can provide scientific and technical insights or design constraints for an effective reservoir understanding and exploitation. Attitude to team working, accurate and meticulous approach to data analysis and management, and an open mindset are essential traits of this course.

Students will acquire:
' Deep knowledge of the technologies and methodologies applied for the characterization of hydrocarbon-bearing formations through reservoir geology and geophysics as well as through the analysis of pressure transients (well tests) and historical production data.
' Profound understanding of the production drives and their implications on the reservoir productivity and hydrocarbon recovery.
' Good knowledge of the improved and enhanced oil recovery methods.
' Ability to identify the key elements of a technical problem in reservoir engineering. Ability to understand, describe and analyze the physical phenomena occurring in a reservoir during production through the governing equations and through the use of analytical models.
' Ability to handle the methods and software adopted worldwide in the oil industry for static and dynamic reservoir numerical simulation based on a good understanding the principles and assumptions of which they rely.
' Ability to define the most adequate production strategies based on technical, economic and environmental indicators, also with the aid of scientific software, graphs, and other convenient tools or representations.
' Ability to capture the essential messages, the methodologies and their implications from technical papers and manuals.

Students should have a good knowledge of geophysics, geology and geomechanics to be able to truly understand the reservoir earth model, which is the basis for describing the reservoir dynamic behavior and for subsequent simulations of the production performance. It is essential that students master the concepts and the basics of rock and fluid properties and their mutual interactions, the flow equations, and the pressure analysis and interpretation techniques. Familiarity with the orders of magnitude of the most relevant quantities (fluid properties, petrophysical characteristics, fluid-rock interaction properties, hydrocarbon recovery factors,') is required.

' Material balance for oil and gas reservoirs
' Pressure Transient Analysis
o well damage
o oil well productivity
o gas well deliverability
o straight lines analysis
o pressure derivative analysis
o numerical well test modeling
' Water and gas injection
o immiscible displacement
o operational issues
' Reservoir numerical modelling
o static modelling
o dynamic modelling
o set-up and calibration of a reservoir model, and simulation of the reservoir dynamic behavior under different development scenarios
' Impact of geomechanics on reservoir behavior
' Decline curve analysis

Exercises will include application of the methodologies presented and discussed during lectures to case studies based on synthetic and real data, with increasing degree of complexity. The software commonly adopted in the oil industry for well test analysis and for reservoir simulation will be used. During the course the complete workflow of characterization, history match and production forecast of an oil reservoir with gas cap and water drive will be developed through static and dynamic modeling. Under the guidance of the professor, students will be encouraged to work independently.

Reference books:
Tarek H. Ahmed, 2006. Reservoir engineering handbook, Elsevier/Gulf Professional
L. P. Dake, 1983. Fundamentals of reservoir engineering, Elsevier
Bourdet D., 2002. Well test analysis: the use of advanced interpretation models. Elsevier.
Horne R., 2001. Computed aided well test analysis, Stanford University, Petroway Inc.

Technical Papers will be provided (free download from the SPE One-Petro library is also available)
The slides presented during lectures will be periodically posted on the website

The assessment of acquired knowledge and technical skills occurs through written exams on the theoretical parts and on applicative aspects. The exam will comprise true/false options, multiple choice questions, open questions, and problem solving. The exam is closed books.
Oral integration is up to the students, provided that the minimum marks for passing the exam (18/30) have been reached. The oral integration can modify the score of the written exam by plus or minus 2 point maximum.
The capability to integrate knowledge gained in other courses and contexts, to critically examine a technical problem and to select models and methods to reach the solution is expected.