Geothermal Reservoir Engineering
Geothermal reservoir engineering is that the study of fluid changes during a geothermal reservoir: changes in fluid state (pressure, temperature, and saturation) and fluid flow within the reservoir. Geothermal reservoirs are normally hosted in fractured igneous rock and may be large both in area and in thickness. Reservoir engineering covers two main areas, the testing of wells and construction of a simulation model of the reservoir. Well testing comprises completion testing to spot permeable zones and reservoir pressure, followed warm‐up and discharge to spot reservoir temperature and permeability. Correlation of those measurements across the reservoir is employed , along side geoscientific data, to construct a conceptual model. This conceptual model is then quantified as a simulation model, which is calibrated against the wild data and therefore the response to production, if any. [1]
Random sampling with a reservoir
We introduce fast algorithms for choosing a random sample of n records without replacement from a pool of N records, where the worth of N is unknown beforehand. the most results of the paper is that the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in O(n(1 + log(N/n))) expected time, which is optimum, up to a continuing factor. Several optimizations are studied that collectively improve the speed of the naive version of the algorithm by an order of magnitude. We give an efficient Pascal-like implementation that comes with these modifications which is suitable for general use. [2]
Fractured Reservoir Simulation
This paper describes the event of a three-dimensional, three-phase model for simulating the flow of water, oil and gas during a naturally fractured reservoir. A dual porosity system is employed to explain the fluids present within the fractures and matrix blocks. Primary flow within the reservoir occurs within the fractures with local exchange of fluids between the fracture system and matrix blocks. The matrix-fracture transfer function is predicated on an extension of the equation developed by Warren and Root and accounts for capillary pressure, gravity, and viscous forces.
Both the fracture flow equations and matrix-fracture flow are solved implicitly for pressure, water saturation, gas saturation and saturation pressure. [3]
Contribution of changing precipitation and climatic oscillations in explaining variability of water extents of large reservoirs in Pakistan
Major threat that Pakistan faces today is water scarcity and any significant change in water availability from storage reservoirs including below normal precipitation threatens food security of quite 207 million people. Two major reservoirs of Tarbela and Mangla on Indus and Jhelum rivers are studied. Landsat satellite’s data are wont to estimate the water extents of those reservoirs during 1981–2017. A long-term significant decrease of 15–25% decade−1 in water extent is found for Tarbela as compared to 37–70% decade−1 for Mangla, mainly during March to June. Significant water extents reductions are observed within the range of −23.9 to −53.4 km2 (1991–2017) and −63.1 to −52.3 km2 (2001–2010 and 2011–2017) for Tarbela and Mangla, respectively. [4]
Rate Decline-based Models for Gas Reservoir Performance Prediction in Niger Delta Region
This work considers the Decline Curve Analysis (DCA) approach as a fast tool to estimate the gas reservoir performance of field “ABC” within the Niger Delta region. the traditional Arps’ models: Exponential, Harmonic and Hyperbolic, alongside with the Reciprocal and Quadratic models were used. Production data: gas production rate (qt) and gas cumulative production (GP) were obtained from 13 wells within the field “ABC”. Multivariate analyses were performed with the mentioned models to determine the decline constant (Di) and decline exponent (b); for hyperbolic model, of the sector “ABC” within the Niger Delta region. A decline constant of 0.000064day-1 was obtained from all the models with exception of Reciprocal model with 0.00053day-1 for the gas field. Also, the decline exponent (b) obtained for Hyperbolic model was 0.9999. [5]
Reference
[1] Grant, M.A., 2015. Geothermal reservoir engineering. Handbook of Clean Energy Systems, (Web Link)
[2] Vitter, J.S., 1985. Random sampling with a reservoir. ACM Transactions on Mathematical Software (TOMS), (Web Link)
[3] Thomas, L.K., Dixon, T.N. and Pierson, R.G., 1980, January. Fractured reservoir simulation. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. (Web Link)
[4] Contribution of changing precipitation and climatic oscillations in explaining variability of water extents of large reservoirs in Pakistan
Ibrar ul Hassan Akhtar & H. Athar
Scientific Reports volume 9, (Web Link)
[5] Okon, A., Olagunju, D. and Akpabio, J. (2017) “Rate Decline-based Models for Gas Reservoir Performance Prediction in Niger Delta Region”, Current Journal of Applied Science and Technology, 19(1), (Web Link)