Completion Length Optimization in Gas Wells

Armenta, Miguel; White, Christopher D.; Wojtanowicz, Andrew K.

doi:10.2118/2003-192

Cited by 7 publications

(3 citation statements)

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“…The gas-water relative permeability curves are for water-wet system and the capillary pressure is assumed negligible due to high contrasts in gas and water densities and the relatively high permeability considered in this study. 20 Production is constrained by a constant tubing head pressure and a terminated if rate is lower than 1 MMscf/day.…”

Section: Benefits Of Oa/noamentioning

confidence: 99%

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Efficient Design of Reservoir Simulation Studies for Development and Optimization

Kalla

White

2005

All Days

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Section: Benefits Of Oa/noamentioning

confidence: 99%

“…The importance of the non-Darcy factor is especially noteworthy, as this effect occurs are modes rate, and this effect is often neglected (or represented using a lumped non-Darcy skin, which does not have an equivalent effect on gas coning). 20 …”

Section: Response Model Building and Sensitivity Studiesmentioning

confidence: 99%

Efficient Design of Reservoir Simulation Studies for Development and Optimization

Kalla

White

2005

All Days

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“…As noted in Karpur et al (2000), "If too few bins are selected, the facies occurrence probability (FOP, referred to in this paper as posterior probability) lacks the ability to discriminate between adjacent log readings; if there are too many bins, the FOP will not be estimated precisely." Alternatively, the BBM (Tang et al 2004;Armenta et al 2003) can overcome the above dilemma. This method uses an empirical beta function to fit the distribution of f (L | Q i ) instead of selecting discrete bins.…”

Section: Introductionmentioning

confidence: 99%

Incorporation of Spatial Characteristics Into Volcanic Facies and Favorable Reservoir Prediction

Tang

Zhang²

2006

SPE Reservoir Evaluation &Amp; Engineering

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Compared to clastic reservoirs, volcanic reservoirs exhibit higher heterogeneity. Lithological facies type is one of the most important indicators of favorable volcanic reservoirs. Traditionally, facies are identified by core observation or log classification. However, spatial-distribution characteristics and geological conceptual models, which are important in the early stages of exploration, are seldom incorporated quantitatively in facies prediction. Based on previous work, a new method has been developed to incorporate volcanic spatial information with limited well data (three wells) to improve facies prediction. This method was applied to a volcanic clastic reservoir of the Cretaceous Yingchen member of the Xinshan fault depression, northeastern China. For better well control, an artificial neural network (ANN), a beta-Bayesian method (BBM), and a discriminant analysis (DA) algorithm, were used to predict log-based facies. Confidence analysis was applied to evaluate the log facies prediction. Analysis of variance (ANOVA) verifies that the overall prediction accuracy is above 82%.Indicator kriging was used to estimate the conditional probabilities of facies occurrence given residual thickness. This is based on the assumption that the residual thickness of the volcanic formation is controlled by distance from the eruption center, a major factor defining the geological facies. The geological conceptual models (areal sedimentary facies maps and diagenetic facies maps) were converted into the conditional probability of facies occurrence in given geological settings using multinomial logistic regression. These conditional probabilities were combined with well-log facies data within a Bayesian framework. Three favorable reservoirs were predicted based on the method above, and the predictions were proved by the subsequent drilling. Description of MethodsBecause volcanic lithofacies consist of complex mineral components and are vulnerable to diagenetic transformation, traditional lithology-interpretation methods using a single-log curve such as spontaneous potential (SP) or gamma ray (GR) are not adequate. Multivariate statistics and ANNs can use multiple logs to quantify lithofacies and are used widely for complex facies classification. One purpose of this paper is to compare the performances of three

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Feasibility Evaluation of Using Downhole Gas-Water Separation Technology in Gas Reservoirs with Bottom Water

Radwan¹

2017

Day 3 Wed, March 08, 2017

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Many gas reservoirs are water driven. Water supplies an extra mechanism to produce the gas reservoir, but it can create production problems in the wellbore. These water production problems are more critical in low productivity gas wells.Water loads up the gas well, when a lot of gas remains in the reservoir. Conventional technologies of gas well dewatering remove water from inside the wellbore without controlling water at its source. Downhole gas-water Separation (DGWS)technolgy was developed. It separates gas from produced water inside the wellbore and injects the produced water into the disposal zone. Two types of separators have been used hydrocyclone and gravity separators. The aim of this work is to evaluate the using of DGWS for gas wells comparing simulated performance of a conventional well. The simulation runs were made over a various range of the initial reservoir pressure and permeability values for a gas reservoir with bottom water drive. The results show considerable incremental recovery of a DGWS over conventional - a 3.3 - fold recovery increase for reservoir with 1500 psia pressure and permeability 1 md. The study also identified the challenges during DGWS applications. A good candidate well for DWGS installation must have a high-quality disposal zone with sustainable permeability with good vertical and mechanical isolation between the production and injection formations. Finally, downhole gas-water separation technology has a great potential in gas well dewatering and reservoir development especially in low pressure-low permeability gas reservoirs.

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Completion Length Optimization in Gas Wells

Cited by 7 publications

References 0 publications

Efficient Design of Reservoir Simulation Studies for Development and Optimization

Efficient Design of Reservoir Simulation Studies for Development and Optimization

Incorporation of Spatial Characteristics Into Volcanic Facies and Favorable Reservoir Prediction

Feasibility Evaluation of Using Downhole Gas-Water Separation Technology in Gas Reservoirs with Bottom Water

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