Rizzato, Paolo (Eni S.p.A.) | Castano, Daniele (Eni S.p.A.) | Moghadasi, Leili (Eni S.p.A.) | Renna, Dario (Eni S.p.A.) | Pisicchio, Patrizia (Eni S.p.A.) | Bartosek, Martin (Eni S.p.A.) | Suhardiman, Yohan (Eni Australia Ltd.) | Maxwell, Andrew (Eni Australia Ltd.)
This paper describes the results of an integrated reservoir study aimed at producing hydrocarbons through a sustainable development from a green High Temperature (HT) giant CO2-rich gas field in the Australian offshore. The development concept addressed the complex challenge of exploiting resources while minimizing the carbon impact.
In order to characterize the reservoir in the most detailed way and to describe the fluids behaviour, a 1.8 million active cells compositional model has been built. An analytical aquifer has been coupled in order to represent the boundary conditions of the area.
The faults system, interpreted on seismic data by geophysicists, has been included in the simulation model. The selected development plan includes the re-injection of the produced CO2 into the aquifer of the reservoir itself. The supercritical CO2-brine relative permeability curves at reservoir conditions have been provided by Eni laboratories, where the experiments were performed.
Therefore, a detailed model has been built with the purpose of: Defining producing well and CO2 injector well locations, numbers and phasing to evaluate expected CO2 injectivity and CO2 breakthrough issues; Optimizing the development concept through a risk analysis approach; Estimating the CO2-rich gas injectivity and storage capacity in the saline aquifer of the reservoir; Predicting the behavior of the CO2-rich gas after re-injection (breakthrough timing and plume migration); Maximizing the CO2 sequestration in the reservoir.
Defining producing well and CO2 injector well locations, numbers and phasing to evaluate expected CO2 injectivity and CO2 breakthrough issues;
Optimizing the development concept through a risk analysis approach;
Estimating the CO2-rich gas injectivity and storage capacity in the saline aquifer of the reservoir;
Predicting the behavior of the CO2-rich gas after re-injection (breakthrough timing and plume migration);
Maximizing the CO2 sequestration in the reservoir.
Quantification of multi-phase flow processes taking place in natural porous and fractured rocks has a remarkable relevance to economically sustainable management and viable development of oil- and gas-bearing geologic formations. Simultaneous flow of two- and three- fluid phases in porous media is typically based on a continuum scale description which imbues relative permeabilities as key system parameters to be estimated and linked to fluid saturations. Estimates of relative permeabilities are then employed to support better the quantification of productivity, injectivity, and ultimate recovery from reservoirs. In this work, we report the results of laboratory-scale three-phase relative permeabilities on diverse core samples. We also investigate the saturation history dependency (i.e. hysteresis) of three-phase relative permeability during under simultaneous water and gas injection.
Three-phase relative permeability data are obtained at different condition by the way of a Steady-State (SS) technique. We use direct X-Ray technique to assess the spatial and temporal dynamics of in-situ saturations. Dual X-Ray energies are employed to assess the SS three-phase saturations. The use of in-situ X-Ray scanning technology enables us to accurately measure fluid displacement during the core-flooding test. The SS three-phase experiments are condcuted by following various saturation paths including CDI, DDI and IDI. The C, D and I letters represent as Constant, Increasing and Decreasing (i.e., CDI saturation variation abbreviates Constant water, Decreasing oil and Increasing Gas).
We observe in all the of the tests, water relative permeabilities display an approximately linear dependence on the logarithm of its own saturation and show no dependency upon saturation history.
Three-phase oil relative permeability appears to be varied with all saturations and be dependent on all saturations phases. Gas three-phase relative permeability was affected more by saturation history than other phases. However in the test where the gas fractional flow was increased, the dependency of gas relative permeability on gas saturation was observed.
As only a limited quantity of complete three-phase data are available, this study stands as a reliable reference for further model development and testing.
AbstractThis work is focused on exploring the applicability of intelligent methods in assessing porosity and permeability in the context of reservoir characterization. The main motivation underlying our study is that appropriate estimation of reservoir petrophysical parameters such as porosity and/or permeability is a key step for in-situ hydrocarbon reservoir evaluation. We ground our analysis on information on log-depth, caliper, conductivity, sonic logging, natural gamma, density and neutron porosity, water saturation, percentage of shale volume, and type of lithology collected from well loggings in an oil field in the middle-east (a total number of 11 exploratory wells are considered). Data also include porosities and permeabilities evaluated on core samples from the same wells. All these data are embedded in a neural network-based approach which enables us to establish input-output relationships in terms of an optimized number of input variables. Three diverse intelligent techniques are tested. These include: (i) classical artificial neural networks; (ii) artificial neural networks based on principal component analysis (PCA) transformation; and (iii) statistical neural networks based on a bagging approach. Our results suggest that the statistical neural network is most effective for the field setting considered. The application of this neural network with 9 input parameters provides reliable performances in 94% and 81% of the cases, respectively in the training and validation phases, for the estimation of porosity. A trained network with 10 input parameters leads to successfull reproduction of permeability values in 85% and 79.5% of the cases, respectively during training and validation of the network. Results from this study are expected to be transferable to applications involving evaluation of petrophysical properties of a target reservoir in the presence of incomplete well log datasets.