The Kurunda Field Tirrawarra Reservoir: A Case Study of Retrograde Gas-Condensate Production Behavior

Hueni, Greg; Hillyer, M.G.

doi:10.2118/22963-ms

Cited by 4 publications

(3 citation statements)

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“…An increase in the molecular weight leads to an increase in the CGR value. This finding is in agreement with several previously published research. − ,− In addition, impact of dewpoint pressure on the CGR supports the validity of the empirical correlations available in the literature. − ,− Increase in dewpoint pressure results in an increase in the value of CGR. It should be also noted here that as the reservoir temperature increases, the magnitude of the CGR ...…”

Section: Resultssupporting

confidence: 91%

“…In addition to the data generated from the experimental part, some data from the literature were also used in developing the PSO-ANN model for CGR prediction. A part of the data used in this study is presented in Appendix A, and the rest can be tracked in the open literature. ,,,,− ,− …”

Section: Experimental Procedures and Data Collectionmentioning

confidence: 99%

“…A set of 295 experimental data was collected to develop and examine the proposed PSO-ANN model. The data series includes 45 data from South Pars gas-condensate reservoir and 250 data from the literature. ,,,,− ,− The ranges of the parameters used to develop the PSO-ANN are given in Table .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

et al. 2012

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Condensate-to-gas ratio (CGR) plays an important role in sales potential assessment of both gas and liquid, design of required surface processing facilities, reservoir characterization, and modeling of gas condensate reservoirs. Field work and laboratory determination of CGR is both time consuming and resource intensive. Developing a rapid and inexpensive technique to accurately estimate CGR is of great interest. An intelligent model is proposed in this paper based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) technique. The PSO-ANN model was evaluated using experimental data and some PVT data available in the literature. The model predictions were compared with field data, experimental data, and the CGR obtained from an empirical correlation. A good agreement was observed between the predicted CGR values and the experimental and field data. Results of this study indicate that mixture molecular weight among input parameters selected for PSO-ANN has the greatest impact on CGR value, and the PSO-ANN is superior over conventional neural networks and empirical correlations. The developed model has the ability to predict the CGR with high precision in a wide range of thermodynamic conditions. The proposed model can serve as a reliable tool for quick and inexpensive but effective assessment of CGR in the absence of adequate experimental or field data.

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Section: Resultssupporting

confidence: 91%

Section: Experimental Procedures and Data Collectionmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

et al. 2012

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Hydraulic Fracturing Down Under

2007

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This paper reviews the fracturing history in the Cooper Basin and summarizes the results of over 650 fracturing treatments characterised by high tectonic stresses, high fracturing pressures, high reservoir temperatures, and stacked reservoir lithologies of sands, shales, and coals. Initial treatments targeted multiple moderately high permeability (1–10 md) formations, while multi-staging operations are now targeting single, less extensive, lower quality reservoirs. The paper discusses the techniques used for predicting and designing for natural fracture leakoff, high near wellbore pressure losses and high fracture gradients. It shows the changes in fracturing ideologies and how they have altered the completion strategies, predicted fracture geometries, fracturing materials, treatment schedules, and post-frac production. Introduction Hydraulic fracturing began in the Cooper Basin in 1968 and has been a critical technology in the development of its gas and oil reserves. Although fracturing has been used extensively in other regions of Australia such as the small to medium sized oil fields of the Eromanga Basin in Central Australia and the Coalbed regions of Eastern Australia, this paper is focused on the fracturing experience in the more extreme conditions of the Cooper Basin. Cooper Basin Description. The basin is a Late Carboniferous to Middle Triassic, non-marine sedimentary environment, which underlies the desert region of Eastern-Central Australia (Figure 1). It is characterised as fluvio-lacustrine, with fining upward sandstones, siltstones, interbedded shales and coals. Deposition varies between braided and meandering fluvial sands & alluvial fans, distributary channels, and crevasse splays1. Figure 1 - Location of Cooper Basin (Blue) and Overlying Eromanga Basin (Green) The basin is the primary on-shore producing area in Australia for natural gas, while also producing significant oil and LPG. It extends over a region of 50,000 sq. miles (130,000 km2) and contains over 120 separate gas fields and 10 oil fields. Production from Cooper Basin reservoirs is presently 600 MMscf/day from 700 gas wells and 2,500 bopd from 50 oil wells. Estimates for recoverable oil and gas reserves are 43.9 MMstb and 8.2 tcf, with remaining reserves (as of 1998) of 14.8 MMstb and 3.6 tcf1. Reservoir depletion effects on fracturing can be significant and will be presented later in the paper. A graph of the basement depth structure is presented in Figure 2 and shows the major basin details and fractured well locations. The primary features are the Nappamerri Trough that is located in the middle of the basin and the various ridges that surround the Trough. Most of the significant fields are located on the North-West, South, East, and North-East ridges. Previous publications have been written concerning the Tirrawarra field2–3 which is located in the North-West ridge and the Kurunda Field4 which is located in the South-East ridge. Other publications describe general basin reservoir characteristics and/or fracturing behaviour5–12.

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Parametric Study of Gas-Condensate Reservoir Behavior During Depletion: A Guide for Development Planning

Bourbiaux

1994

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The results of systematic compositional simulations of gas-condensate depletion in a radial and cross-sectional reservoir model are presented. The effects on productivity, recoveries and fluid distribution are analyzed and quantified when changing various parameters, such as the role of non-Darcy flow, the permeability, the relative permeabilities (kr) in relation with the interfacial tension (IFT), the nature of the fluid. Below the dew point pressure, the drop in gas productivity is linked to the development of a condensate ring around the wellbore. Gas and condensate kr and their dependence on IFT control the time or pressure at which condensate buildup becomes significant and gas productivity drops. Tight reservoirs with lean gas-condensates may pose early and drastic productivity problems. Far from wellbore, condensate may segregate under the low-IFT conditions met during depletion, especially if the reservoir permeability is high and the fluid is rich. In a depletion process, condensate recovery is all the lower as the condensate content of the fluid is higher. The results of this study can be used as a guide during the development planning of a gas-condensate field (1) to estimate the quantitative role played by key rock-fluid parameters during future exploitation, (2) to define the most relevant core-fluid measurements and simulation studies for reducing uncertainties on field performance predictions. INTRODUCTION The industrial importance of gas-condensate fields is considerably increasing in the present years because many such fields have been discovered and are now under development or awaiting development. In 1988, Hill1 mentioned that only 3 out of the 35 to 40 discovered gas-condensate fields of the United Kingdom sector of the North Sea were to be developed at that time, and that proven and probable condensate reserves could be of one billion barrels in that area. A recent review of upcoming field developments2 indicates that the start-up of more than 15 gas-condensate fields in the United Kingdom sector of the North Sea and of more than 10 in the Norwegian sector is anticipated before the end of this decade. The profitability of the development of gas-condensate fields depends on both gas and condensate production profiles. According to the condensate content, the field environment, and technical or commercial factors, priority is given to a maximum and early production of condensate and/or to an optimal gas production with condensate as a by-product. A good exampe of the interrelated role of these factors is shown with the development of Brae fields complex.3 Hence, two major reservoir engineering problems have to be considered during development studies,the possible drastic drop in the gas productivity of wells below the dew point pressure,the loss of condensate trapped throughout the reservoir at the end of exploitation.

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The Kurunda Field Tirrawarra Reservoir: A Case Study of Retrograde Gas-Condensate Production Behavior

Cited by 4 publications

References 1 publication

Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

Hydraulic Fracturing Down Under

Parametric Study of Gas-Condensate Reservoir Behavior During Depletion: A Guide for Development Planning

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