A Cost-Effective Method to Maximize the Hydrocarbon Recovery by Optimizing the Vertical Well Placements through the Simulation Opportunity Index

Saputra, Wardana; Ariadji, Tutuka; Patzek, Tadeusz W

doi:10.2118/182803-ms

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…At present, the productivity analysis of deviated and horizontal wells, the determination of a reasonable working system, and the design of lifting technology [16][17][18][19][20] are mainly based on a series of Vogel IPR equations under different well inclination angles proposed by Cheng in 1990 [21] on the basis of reservoir numerical simulations. It is not clear whether other commonly used IPR equations, such as the Vogel equation (derived in 1968, 2012) [22][23][24], the Klins-Majcher equation (derived in 1989) [25], the Bendakhlia-Aziz equation (derived in 1989) [26], and the Wiggins-Russell-Jennings equation (derived in 1992) [27] (listed in Table 1), can be used for the productivity evaluations of inclined or horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

Optimal Inflow Performance Relationship Equation for Horizontal and Deviated Wells in Low-Permeability Reservoirs

Wang

Shao

et al. 2021

Geofluids

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After Vogel proposed a dimensionless inflow performance equation, with the rise of the horizontal well production mode, a large number of inflow performance relationship (IPR) equations have emerged. In the productivity analysis of deviated and horizontal wells, the IPR equation proposed by Cheng is mainly used. However, it is still unclear whether these inflow performance models (such as the Cheng, Klins-Majcher, Bendakhlia-Aziz, and Wiggins-Russell-Jennings types) are suitable for productivity evaluations of horizontal and deviated wells in low-permeability reservoirs. In-depth comparisons and analyses have not been carried out, which hinders improvements in the accuracy of the productivity evaluations of horizontal wells in low-permeability reservoirs. In this study, exploratory work was conducted in two areas. First, the linear flow function relationship used in previous studies was improved. Based on the experimental pressure-volume-temperature results, a power exponential flow function model was established according to different intervals greater or less than the bubble point pressure, which was introduced into the subsequent derivation of the inflow performance equation. Second, given the particularity of low-permeability reservoir percolation, considering that the reservoir is a deformation medium, and because of the existence of a threshold pressure gradient in fluid flow, the relationship between permeability and pressure was changed. The starting pressure gradient was introduced into the subsequent establishment of the inflow performance equation. Based on the above two aspects of this work, the dimensionless IPR of single-phase and oil-gas two-phase horizontal wells in a deformed medium reservoir was established by using the equivalent seepage resistance method and complex potential superposition principle. Furthermore, through regression and error analyses of the standard inflow performance data, the correlation coefficients and error distributions of six types of IPR equations applicable to deviated and horizontal wells at different inclination angles were compared. The results show that the IPR equation established in this study features good stability and accuracy and that it can fully reflect the particularity of low-permeability reservoir seepage. It provides the best choice of the IPR between inclined wells and horizontal wells in low-permeability reservoirs. The other types of IPR equations are the Wiggins-Russell-Jennings, Klins-Majcher, Vogel, Fetkovich, Bendakhlia-Aziz, and Harrison equations, listed here in order from good to poor in accuracy.

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Section: Introductionmentioning

confidence: 99%

Optimal Inflow Performance Relationship Equation for Horizontal and Deviated Wells in Low-Permeability Reservoirs

Wang

Shao

et al. 2021

Geofluids

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Reservoir Fluids Saturation Diagnostic and Hydrocarbon Targets Identification Workflow

Faqehy

Katamish

Al-Ismael

2017

SPE Middle East Oil &Amp; Gas Show and Conference

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Though there are various methods to assess reservoir performance, historical methods seem to focus the assessment on a single or couple of parameters. These include traditional methods to evaluate the reservoir sweep through average oil saturation-thickness maps, remaining oil volume maps, etc. Optimum reservoir management is a challenging and time consuming process since it usually involves analyzing many reservoir properties such as porosity, permeability, thickness, hydrocarbon saturation, fluid properties, relative permeability, net-to-gross ratio and pressures. In this work, we incorporate all these parameters into an automated workflow for reservoir diagnostics; and identification and ranking of optimum hydrocarbon (HC) targets. The proposed workflow extracts static and dynamic information from reservoir simulation outputs and performs additional post-processing calculations on each grid cell for all time steps. The methodology involves classification of the reservoir simulation grid cells based on fluid saturation, relative permeability, pressure changes and displacing phase fluxes. After that, Produced, Mobile and Immobile oil volumes are calculated for each cell. These volumes are then grouped into six categories, namely, Produced, Highly Contacted, Moderately Contacted, Minimally Contacted, Uncontacted and Immobile Oil. In addition, the workflow incorporates different indicators for determining grid cell quality. These indicators are Reservoir Opportunity Index (ROI) and Simulation Opportunity Index (SOI); and we proposed a new reservoir quality indicator that incorporate changes in pressure over time. Finally, the workflow identifies connected cells with high quality indices and ranks these regions based on size and/or grid cell quality as potential targets for infill drilling. The presented automated workflow is introduced as an integral part of well placement optimization workflow. It has been tested on several simulation models and successfully identified and ranked un-swept reservoir regions which proved through dynamic simulations to be credible future drilling targets.

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Well Optimisation Technique with Backward Elimination Process for Greenfield Gas Development

Vardcharragosad¹,

Doungprasertsuk²,

Srisuriyon³

et al. 2023

Day 3 Fri, March 03, 2023

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The well optimization technique with backward elimination aims to determine the optimum number of wells and their locations that can maximise project value and its recoverable resources, through repeated ranking of candidate wells and eliminating the poorest performer. For a greenfield development, subsurface uncertainties are typically still very large due to limited data from exploration and appraisal wells. This study outlines our approach to perform well optimization with these governing uncertainties in order to support the decision-making process. First, multiple realizations of reservoir models are constructed to represent range of possible outcomes by sampling different values from uncertainty parameters. Backward elimination for well optimization is then performed on those realizations. Wells can be ranked based on means and standard deviations of their performance, and the lowest rank candidate is eliminated from the process one at a time. At this point, project economic and resources are evaluated to find optimum set of wells for field development. Furthermore, well performance data from multiple realization models are carefully analyzed to define key subsurface uncertainties that need to be managed. Solution from this backward elimination with subsurface uncertainty workflow can maximize project valuation because it balances the risk of overspending to drill sub-optimum wells in some realizations with the risk of losing sell opportunity due to insufficient field deliverability in the other realizations. Development decision will be more robust because it is based on the optimum configuration that is applicable irrespective of the unknown uncertain quantities. Moreover, detailed analysis on well performance data allows us to better understand the risk associated with our planned wells so that appropriate de-risking plan can be developed and combined into development strategy. The backward elimination process is straightforward to implement and normally does not require very high computational expense. Thus, it is suitable to be used with uncertainty workflow with multiple realizations of reservoir models, which will increase computational requirement by multiple times. Other commonly used techniques for well optimization such as a Genetic Algorithm (GA) or an Evolutionary Algorithm (EA) are computational expensive by themselves already; and they will require even more runtime when using them with this uncertainty workflow. This paper extends backward elimination approach for well optimization to be used with uncertainty workflow. The overall uncertainty analysis workflow is discussed and provided, with key steps detailing the approach taken. Project valuation and recoverable resources can be further optimized with this new approach, and ultimately can guide the decision making in field development.

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A Cost-Effective Method to Maximize the Hydrocarbon Recovery by Optimizing the Vertical Well Placements through the Simulation Opportunity Index

Cited by 3 publications

References 6 publications

Optimal Inflow Performance Relationship Equation for Horizontal and Deviated Wells in Low-Permeability Reservoirs

Optimal Inflow Performance Relationship Equation for Horizontal and Deviated Wells in Low-Permeability Reservoirs

Reservoir Fluids Saturation Diagnostic and Hydrocarbon Targets Identification Workflow

Well Optimisation Technique with Backward Elimination Process for Greenfield Gas Development

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