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Abstract This study expands upon the use of modified-Hall analysis to discern the characteristics of a high-permeability channel. Briefly, the modified-Hall plot uses three curves involving improved Hall-integral and the two derivatives, analytic and numeric. Ordinarily, the derivative curves overlay on the integral curve during matrix injection, but separates lower when fracturing occurs. This work presents a method to identify and characterize high-conductive layers or channels between injector and producer pairs with the modified-Hall analysis. The distance separating the integral and derivative curves provides the required information to quantify channel properties. A simple analytical solution is presented for transforming the separation distance into channel permeability-thickness product. The analytic derivative is based on the radial-flow-pattern assumption and the numeric derivative is correlated to the pressure response. Therefore, a comparison of these two curves reveals clues about the maturity of a waterflood at a given time. Several simulated examples verified the channel-property-estimation algorithm and identified the distinctive derivative signatures for channeling and fracturing situations. This methodology is also useful for identification of wormhole propagation during sand production in unconsolidated formations. Introduction The success of any waterflood depends largely upon the ability to bank oil for efficient sweep to occur, regardless of the mobility ratio. Presence of reservoir heterogeneity simply compounds the volumetric sweep issue. While heterogeneity can manifest in many forms, this study focuses on identifying and characterizing high-permeability streaks or high-conductive fractures providing the preferential flow path. Understanding the presence of such preferential flow conduits help manage a waterflood by pattern realignment, recompletion, among other measures to improve the volumetric sweep efficiency. Of course, the characterization of a thief zone can immensely aid any flow-simulation study attempting to explain premature breakthrough. Among the tools available for injection-well monitoring, the conventional Hall analysis (1963) is quite popular. Ordinarily, lack of its sensitivity has prompted others to offer improvements over the years. Some of the notable contributors include Buell et al. (1990), who suggested the use of both bottomhole injection pressure and reservoir pressure instead of the wellhead pressure alone, as used in the conventional Hall plot. Evaluation of the reservoir pressure from a slope-analysis method was offered by Silin et al. (2005a, 2005b). Ideally, the Hall method is suitable for either early injection period or during the post-breakthrough period, because the notion of single-reservoir pressure is entertained. More recently, Izgec and Kabir (2009) offered a new formulation of the Hall analysis. To that end, the development of an analytic derivative expression turns out to be much more discriminating for yielding the desired diagnostic clues. Ascertaining variable radial distance of the injection bank and pressure at the water/oil interface (pe) made the new formulation robust and suitable for prebreakthrough situations. That study also showed that pe practically becomes time invariant in postbreakthrough situation, suggesting applicability of the original Hall formulation. In this study we show that the Hall integral and its numerical derivative become parallel when a high-permeability conduit is intercepted, and their degree of separation is a measure of permeability-thickness product of the channel-dominated system. We also show that step-rate testing, ordinarily conducted to establish formation-parting pressure, can reveal clues about the contribution of additional layers owing to increased pressure. In fact, distinguishing fracturing from channeling is established by comparing and contrasting both the analytic and numeric derivatives when breakthrough occurs.
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.47)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Dampier Basin > WA-209-P > Stag Field (0.99)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Dampier Basin > WA-15-L > Stag Field (0.99)
Abstract Application of fast, simple and yet powerful analytic tools, capacitance-resistive models (CRMs), are demonstrated with four field examples. Most waterfloods lend themselves to this treatment. This spreadsheet-based tool is ideally suited for engineers who manage daily flood performance. We envision CRM's application to precede any detailed full-field numerical modeling. We have selected field case studies in a way to demonstrate CRMs capabilities in different settings: a tank representation of a field, its ability to determine connectivity between the producers and injectors, and understanding flood efficiencies for the entire or a portion of a field. Significant insights about the flood performance over a short period can be gained by estimating fractions of injected fluid being directed from an injector to various producers and the time taken for an injection signal to reach a producer. Injector-to-producer connectivity may be inferred directly during the course of error minimization. Because the method circumvents geologic modeling and saturation matching, it lends itself to frequent usage without intervention of expert modelers. Introduction History matching reservoir performance is a difficult inverse problem. Ordinarily, history matching entails minimizing the difference between the observed and computed response in terms of gas/oil ratio, water/oil ratio, and reservoir drainage-area pressures. Systematic approaches have emerged to simplify history matching because manual matching by adjusting global and/or local geological and flow properties is tedious and time-consuming. Two classes of matching algorithms have emerged; one dealing with an automated approach involving error minimization, and the other dealing with 3D streamline assisted property adjustments in a systematic way. Some of the automated methods used for history matching include a gradient-based approach (Thomas et al. 1972, Chen et al. 1974, Bissell et al. 1997, Yang and Watson 1998, Zhang et al. 2000, and Gomez et al. 2001), sensitivity-analysis technique (Hirasaki 1973, Dogru and Seinfeld 1981, and Watson 1989), stochastic modeling technique (Tyler et al. 1993 and Calatayud et al. 1994), and optimal-control theory (Chavent et al. 1975 and Wasserman et al. 1975). In addition, history matching with streamlines (Milliken et al. 2001, Cheng et al. 2007) has gained popularity for its computational speed. Because history matching with a single geologic model does not assure attaining the 'correct' model, uncertainty in forecasting remains. Tavassoli et al. (2004) made this point very eloquently. The lack of forecasting certainty has prompted some to pursue history matching and forecasting with ensemble of models carrying geologic uncertainty. For instance, Landa et al. (2005) by using clustered computing showed how uncertainty in static modeling can be handled in both history matching and forecasting phases. Similarly, Liu and Oliver (2005) explored applications of ensemble Kalman filter in history matching where continuous model updating with time is sought for an ensemble of initial reservoir models. In yet another approach, Sahni and Horne (2006) have used wavelets for generating multiple history-matched models using both geologic and production data uncertainty. In spite of the advances made in automated-history matching with grid-based simulations, manual history matching is the norm in most business settings. The purpose of this study is two-fold; first, to alleviate the tedious task of history matching, manual or automated, by providing clues about producer/injector connectivity, and second, to provide a day-to-day waterflood management tool without the intervention of specialists requiring high-end computing.
- North America > United States > Texas (1.00)
- Asia (0.68)
- North America > United States > California > Los Angeles County (0.28)
- North America > United States > Texas > Permian Basin > Midland Basin > Reinecke Field (0.99)
- North America > United States > Texas > Permian Basin > Central Basin > McElroy Field (0.99)
- North America > United States > California > Los Angeles Basin > Wilmington Field (0.99)
- North America > United States > California > East Wilmington Field (0.99)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Mining (0.34)
Abstract Flow rate metering has less-than-satisfactory track record in the industry; modern sensors offer solution to this vexing problem. This paper offers two methods for estimating flow rates, predominantly from temperature data to complement rate measurements. One approach consists of modeling the entire wellbore and requires both wellhead pressure and temperature, whereas the other uses transient temperature formulation at a single point in the wellbore to compute the total production rate. In the entire-wellbore approach, we use a wellbore model handling steady flow of fluids but unsteady-state heat transfer to estimate production rate, given wellhead pressure and temperature. The model rigorously accounts various thermal properties of the fluid and the formation, including Joule-Thompson heating and/or cooling. In the single-point approach, a single point temperature measurement made anywhere in the wellbore, including at the wellhead, is needed to estimate the mass rate at a given timestep. The method entails full transient treatment of the coupled fluid and heat flow problem at hand. Examples from both gas and oil wells are shown to illustrate the application of the proposed methodology. Good correspondence between the measured and calculated results demonstrates the robustness of the proposed methods. These methods provide important rate information in various settings. For instance, in mature assets they can fill in the information void between tests or replace suspect rate data. Even well-instrumented wells can benefit because the methods can act as a verification tool, particularly in assets where integrated asset models are used to fine-tune rate allocation. In addition, the single-point approach can provide the much needed rate information during pressure-transient tests. Introduction Individual well rates enter into a variety of engineering calculations; paradoxically, the industry has struggled to meter this entity with decent accuracy. In fact, accuracy in flow metering has not kept pace with pressure and temperature measurements. Ordinarily, allocation algorithms are used to assign individual well rates from total production, unless a well is instrumented with a flowmeter. The lack of rigor of these allocation routines pose significant challenges during history matching of reservoir performance, particularly those involving rapidly changing events, such as coning or cresting of gas or water. In this context, test separators do not necessarily alleviate the metering issue simply because of inadequate flow time for large distances between a well and the point of separation, coupled with low-test frequency. To compound the matter further, the industry has lacked motivations for accurate metering of nonessential entities, such as water, and to a large extent gas because of its flaring in many field operations. In a case study, Kabir and Young (2002) discussed some of the issues related to gas and water metering in a typical brown-field operation. To enhance the quality of rate measurements at the individual wells, dedicated metering has evolved over the past two decades. However, direct metering of multiphase fluid flow in a pipe is a difficult proposition because both volume fractions and the individual phase velocities must be ascertained. Accordingly, flowmeters have been developed to handle complete, partial, and no separation of phases. Because gas-volume fraction increases with decreasing pressure, downhole metering at higher pressures can largely mitigate handling of the gas phase. Venturi-type flowmeters, requiring no separation of phases, have gained wide acceptance both in downhole (Webster et al. 2006; Tibold et al. 2000; Brodie et al. 1995) and at surface (Warren et al. 2001, 2003; Retnanto et al. 2001; Pinguet et al. 2006). Venturi-type flowmeter appears to have performed well in a comparative study reported by Busaidi and Bhaskaran (2003). Like the venturi, downhole fiber-optic flowmeters is another nonintrusive device that has undergone considerable field testing (Kragas et al. 2002, 2003). Gas/Liquid Cylindrical Cyclone or GLCC technology (Kouba et al. 2006) separates gas from the liquid phases to facilitate ease of measurement at surface. The liquids are metered on the basis of mass using the Coriolis principle (Liu et al. 1988). Oglesby et al. (2006) reported their field experiences with GLCC while testing high-water-cut wells.
- Asia (1.00)
- Europe > United Kingdom (0.88)
- North America > United States > Texas (0.87)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
- North America > United States > Gulf of Mexico > Central GOM > West Gulf Coast Tertiary Basin > Green Canyon > Block 640 > Tonga Field > Tahiti Well (0.99)
- North America > United States > Gulf of Mexico > Central GOM > West Gulf Coast Tertiary Basin > Green Canyon > Block 640 > Tahiti Field > Tahiti Well (0.99)
- North America > United States > Gulf of Mexico > Central GOM > West Gulf Coast Tertiary Basin > Green Canyon > Block 640 > Caesar Field > Tahiti Well (0.99)
- (4 more...)
Abstract This work presents a complete reformulation of the Hall method involving both pre- and post-breakthrough situations. Two approaches involving both transient and material-balance methods produced very similar solutions, which were verified with the results of coupled geomechanical/fluid-flow simulations. The new formulations allow tracking the expanding water-bank radius from inception to breakthrough. Pressure of this bank at the water/oil interface is evaluated at every timestep, thereby allowing continuous update of the 'external pressure' in Hall's formulation. We show that Hall's formulation is a particular case of the proposed approach. Several simulated and field examples demonstrate the value of reformulated Hall analysis. Because Hall formulation involves an integral, the resultant signature, by nature, is insensitive in revealing clues about subtle changes that may occur during formation fracturing or plugging. We observed that the derivative of modified-Hall integral, obtained analytically, provides definitive signatures about fracturing or plugging. The new interpretation approach is particularly suitable for projects at the inception of flooding. Mature projects can benefit equally from the new tool. Perhaps the biggest appeal of the proposed tool lies in the green fields, where real-time data are readily available. Introduction Significant advances have been made in recording, transmitting, filtering, and interpreting real-time production data. However, data interpretation from injection wells has not gained as much attention. Traditional water-injection well evaluation involves pressure-transient analysis, which predictably improved over the years, as testified by the contributions from Hazebtoek et al., 1958; Kazemi et al., 1972; Marrill et al. 1974; Sosa et al., 1981; Abbaszadeh and Kamal, 1989; Yeh and Agarwal, 1989; and Bratvold and Horne, 1990. Falloff analysis allows estimation of permeability, skin, and drainage-area pressure. Because formation parting is quite common, van den Hoek (2005) presented a method for discerning shrinkage of fracture height or length from falloff tests. While great strides have been made in interpreting falloff tests, Hall (1963) plot appears to be one of the few tools available for ongoing performance monitoring. Others have attempted modifications of the Hall plot by introducing the use of bottomhole flowing and reservoir pressures (Buell et al. 1990) or evaluating the reservoir pressure from a slope-analysis method (Silin et al. 2005a, 2005b). Ideally, the Hall method is suitable for either early injection period or during the post-breakthrough period, because the notion of single reservoir pressure is entertained. Both reciprocal injectivity index or RII (Hearn 1983, Abou-Sayed et al. 2007) and evolving skin (Zhu and Hill 1998) are other alternatives to monitoring real-time well performance. This study presents a new formulation of the Hall analysis. To that end, the development of an analytic derivative expression turns out to be much more discriminating for yielding the desired diagnostic clues. Ascertaining variable radial distance of the injection bank and pressure at the water/oil interface (pe) makes the new formulation robust and suitable for prebreakthrough situation. Our study shows that pe practically becomes time invariant in postbreakthrough situation, suggesting applicability of the original Hall formulation. Problem Statement & Solution Approach This study presents reformulation of the Hall (1963) approach for monitoring performance of water-injection wells. The intrinsic idea was to update the outer-bank pressure or pe at every timestep for proper application of the original Hall formulation. Two approaches are presented for updating pe, transient and pseudosteady-state, as shown in Appendices A and B, respectively. We also obtained an analytic expression for the derivative of the Hall integral, as shown in Appendix C. Using coupled geomechanical/fluid-flow simulations, we present synthetic cases showing fracturing, nonfracturing, and plugging of the formation. In particular, comparing and contrasting the derivative curve with the Hall integral provides the definitive clues. To summarize, the two curves trace the same path in matrix-dominated flow when neither fracturing nor plugging occurs. The derivative curve goes below the Hall curve in fracturing situation and rides above it when formation plugging occurs. Thereafter, field examples corroborate these findings.
- North America > United States > Texas (0.46)
- North America > United States > California (0.46)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract This study presents a simplified two-phase flow model using the drift-flux approach to well orientation, geometry, and fluids. For estimating the static head, the model uses a single expression for liquid holdup, with flow-pattern-dependent values for flow parameter and rise velocity. The gradual change in the parameter values near transition boundaries avoids discontinuity in the estimated gradients, unlike most available methods. Frictional and kinetic heads are estimated using the simple homogeneous modeling approach. We present a comparative study involving the new model as well as those that are based on physical principles, also known as semimechanistic models. These models include those of Ansari et al, Gomez et al., and OLGA. Two other widely used empirical models, Hagedorn and Brown and PE- 2, are also included. The main ingredient of this study entails the use of a small but reliable dataset, wherein calibrated PVT properties minimizes uncertainty from this important source. Statistical analyses suggest that all the models behave in a similar fashion and that the models based on physical principles appear to offer no advantage over the empirical models. Uncertainty of performance appears to depend upon the quality of data input, rather than the model characteristics. Introduction Modeling two-phase flow in wellbores is routine in every-day applications. The use of two-phase flow modeling throughout the project life cycle with an integrated asset modeling network has rekindled interest in this area. Plethora of models, some based on physical principles and others based on pure empiricism, often beg the question which one to use in a given application. Although a few comparative studies (Ansari et al. 1994; Gomez et al. 2000; Kaya et al. 2001) attempt to answer this question, often reliability of the data base has left this issue unsettled. One of the main objectives of this paper is to present a simplified two-phase flow model, which is rooted in drift-flux approach. The drift-flux approach (Hasan and Kabir, 2002, pp. 21โ62, Shi et al. 2005a, 2005b) has served the industry quite well, as exemplified by its simplicity, transparency, and accuracy in various applications. The second objective is to show a comparative study with a few models using a small but reliable data base to get a perspective on relative performance. Here, data reliability stems from two elements: rate and fluid PVT properties. Pressure data are typically gathered with permanent downhole and wellhead sensors while rate data are measured with surface flow meters or test separators. In each case, the black-oil fluid PVT model was conditioned with laboratory data to ensure reliability and consistency. Proposed Model Total pressure gradient during any type of fluid flow is the sum of the static, friction, and kinetic gradients, the expressions for which are given in Eq. A-1 in the Appendix. For most vertical and inclined wells, the static head component-which directly depends on the volume average-mixture density-dominates. Thus, in simple terms, two-phase flow modeling boils down to estimating density of the fluid mixture or gas-volume fraction. Because gas-volume fraction depends on whether the flow is bubbly, slug, churn, or annular, we individually model each flow regime. However, for all flow regimes the gas (or lighter) phase moves faster than the liquid (or heavier) because of its buoyancy and its tendency to flow close to the channel center. This allows us to express in-situ gas velocity as the sum of bubble rise velocity, v8, and channel center mixture velocity, Covm. However, in-situ velocity is the ratio of superficial velocity to volume fraction. Therefore, the generalized form of gas-volume fraction relationship with measured variables- superficial velocity of gas and liquid phases-can be written as Equation (1) For downflow, buoyancy acts in the direction opposite to flow. Thus, in Eq. 1, the negative sign in front of the rise velocity applies to downflow and the positive sign is meant for upflow. While Eq. 1 is universal in its application, the values of the flow parameter Co, and rise velocity v8, are dependent on the type of flow and flow pattern. Table 1 presents these values.
- Europe > United Kingdom (0.68)
- North America > United States > Texas (0.28)
Abstract This paper presents an analytic model for computing the wellbore-fluid-temperature profile for steady fluid flow. Although wells with constant-deviation angle can be handled with existing analytic models, complex well architectures demand rigorous treatment. For example, changing geothermal-temperature gradient and deepwater wells present significant challenges. Additionally, available analytic models rarely provide calculation methods for various required thermal parameters, such as the Joule-Thompson coefficient and fluid expansivity. The approach taken in this study entails dividing the wellbore into many sections of uniform thermal properties and deviation angle. The governing differential equation is solved for each section, with fluid temperature from the prior section as the boundary condition. This piecewise approach makes the model versatile, allowing step-by-step calculation of fluid temperature for the entire wellbore. We present simple, thermodynamically sound approaches for estimating thermal parameters. Good success is indicated when performance of the proposed model is compared with data from three wells; producing two-phase gas/oil mixture, single-phase oil, and single-phase gas. Sensitivity of the estimated fluid temperatures to various thermal properties is also examined using our model. Overall, the effects of Joule-Thompson coefficient and liquid expansivity are found to be significant. Introduction Modeling fluid temperature and density profiles in wellbores is crucial for the design of production tubulars and artificial-lift systems, gathering pressure data for real-time reservoir management, and estimating flow rates from multiple producing horizons with distributed-temperature sensors. Significant advances have occured in wellbore-fluid temperature modeling since the pioneering work of Ramey (1962). Ramey's work addressed single-phase flowing-fluid temperature in a line-source well. In this regard, models of Alves et al. (1992), Sagar et al. (1991), and Hasan and Kabir (1994) are worthy of note. In particular, these models extended application to two-phase flows. Yet, the available analytic models are inadequate for direct application to modern directional wells that traverse formation with significantly varying thermal properties with multiple changes in deviation angles. In such cases, even the simple task of estimating geothermal temperature as a function of measured depth becomes nontrivial. Obviously, geothermal gradient strongly influencs heat loss of wellbore fluids, requiring careful piecewise computation. The solution presented in this paper addresses these issues and lend itself to user-friendly spreadsheet computations, if one so chooses. The Fluid-Temperature Model Analytic Expressions for T f . Temperature difference between the wellbore fluid and the surrounding formation results in energy exchange. A general energy balance for the fluid, either single- or two-phase, may be performed following any standard text on thermodynamics.
Abstract The capacitance-resistive model (CRM) offers the promise of rapid evaluation of waterflood performance. This semianalytical modeling approach is a generalized nonlinear multivariate regression technique that is rooted in signal processing. Put simply, a rate variation at an injector introduces a signal, with the corresponding response felt at one or more producers. CRM uses production and injection rate data and bottomhole pressure, if available, to calibrate the model against a specific reservoir. Thereafter, the model is used for predictions. We focused on three different control volumes for CRMs: the volume of the entire field, the drainage volume of each producer, and a drainage volume between each injector/producer pair. Unlike the numerical simulation approach, the CRMs use only production/injection data to predict performance, which provides simplicity and speed of calculation. Once the CRM is calibrated with historical production/injection data, we use an optimization technique to maximize the amount of oil produced by reallocating water injection rates. To verify CRM predictions, the models were tested against numerical flow-simulation results. Two case studies showed that the CRMs are able to successfully history match, and maximize the amount of oil produced by just reallocating water injection. This study introduces analytical solutions to the fundamental differential equations of the capacitance model based on superposition in time. In so doing, this approach adds flexibility, simplicity, and computational speed to the work presented previously. Introduction The CRM relies upon signal-processing techniques in which injection rates are treated as input signals and production rates are the reservoir response or output signals. Inter-well connectivity as well as response delay constitutes the unknown system parameters. Therefore, the model parameters of the reservoir reflect the connectivity between each injector/producer pair based on the historical injection and production data. Thereafter, performance predictions can be made with the fitted model parameters. In this regard, CRM may also be viewed as a nonlinear multivariate regression analysis tool, which accounts for compressibility and fluid flow in the reservoir (Yousef et al. 2006). Unlike the grid-based numerical-simulation approach, CRM models the reservoir flow behavior in accord with interactions (connectivity) between well pairs. Using injection/production data, Albertoni and Lake (2003) used a linear multivariate regression technique with diffusivity filters to predict the total fluid production of a well based on injection rates. In continuation of Albertoni's work (2002), Gentil (2005) explained the physical meaning of multivariate-regression-analysis constants by expressing the connectivity constant as a sole function of transmissibility. Yousef et al. (2006) showed the improved capability of extracting reservoir properties from injection and production data by introducing the capacitance model. The capacitance model considers the effects of compressibility, pore volume, and productivity index in nonlinear multivariate regression by introducing a time constant to characterize the time delay of the injection signal at the producers. Therefore, connectivity indices and time constants can reflect reservoir and fluid properties between injectors and producers. In this study, we introduce analytical solutions for fundamental differential equation of the capacitance model based on superposition in time. We present these solutions for three different reservoir-control volumes:volume of the entire field, drainage volume of each producer, and drainage volume between each injector/producer pair. These analytical solutions facilitate CRM's application for rapid assessment at different levels of a field study, from a single well, to a group of wells, and to an entire field. CRM's analytical solutions in conjunction with the physical meaning of its weights, its capability to discern reservoir properties, its flexibility in taking timesteps, its simplicity, and speed are major advantages over those presented previously. We present applications of CRM to synthetic and real reservoirs by combining its results with an empirical oil-fractional flow model. This fractional-flow approach, introduced by Gentil (2005) and developed by Liang et al. (2007), allows the maximization of oil production rates by reallocating water amongst the injectors.
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.74)
Abstract Well-developed methodology exists for handling uncertainty for a single reservoir. However, development of multiple fields presents significant challenge when uncertainty in large number of variables, such as gas-in place and liquid yield, occur in each reservoir. Some of the challenges stem from our need to forecast the system behavior involving a coupled reservoir/wellbore/surface (CRWS) network for the entire spectrum of variables so that facilities can be designed for the range of fluid composition and throughput. Of course, assessing well count and sequencing well drills are some of the important objectives. This paper describes probabilistic production forecasting with a compositional CRWS network model for nine reservoirs involved in delivering gas supply to a LNG plant in Nigeria. Our main objective was to use an economic indicator to select optimal design of two main pipelines, each transporting 200 and 300 MMscf/D from the two production platforms, located 15- and 5-km, respectively, from the processing platform. Rate and cumulative profiles showed that sustained deliverability of gas can be realized for about 11 years before the decline occurred in high-permeability reservoirs. In other words, uncertainty in gas-in-place did not surface during the plateau period, only during the decline period lasting another five years, after the first eleven. In contrast, the liquid rates exhibited large uncertainty band throughout, a direct manifestation of the condensate yield issue. The uncertainty band among each of the 12 components aided facilities design. Differences in net present value (NPV) and discounted profitability index (DPI) were used as discriminators for discerning optimal pipe size from the standpoint of project economics. Introduction In recent years, probabilistic forecasting has gained popularity and has become the preferred approach when assessing the value of a project, given the uncertainty of many input variables. Uncertainties arise because both static and dynamic variables are ascertained from rather small volumetric samples of a reservoir and subsequent key variables are estimated from interpretations. Systematic approaches have emerged to account for uncertainty of both static and dynamic variables involving statistical approaches. These methods have been detailed elsewhere for a single reservoir.1โ3 However, very few studies exist when production is sought from multiple reservoirs with uncertainty associated with each one of them.Cullick,4 Narayanan et al.,5 and Cullick et al.6 have presentedcase studies of production forecasting under uncertainty for multiple fields. In their studies, simulation tools were integrated with economic evaluation tools and Monte Carlo algorithm. Optimization was sought for an objective function (NPV, for instance) honoring various constraints. The objective of this study was to investigate the impact of uncertainty in input variables on the production forecast for systems consisting of multiple gas/condensate reservoirs, honoring wellbore constraints. We studied multiple reservoirs with multiple wells producing independently. The complexity arises because of the interactions through the common flowline system. The wellbore model was coupled with the reservoir model to honor wellbore constraints. The surface network interfaced with disparate wells through producing rules or constraints. In this study, the types of uncertainty considered are in-place volume, condensate yield, capital costs, and operating costs. We segmented this study into two phases. In phase 1, we developed an analytic simulator to generate the pressure and production forecasts for dry-gas reservoirs, coupled with a simple economic model but without the surface network. The intrinsic idea was to establish well count with a simplistic approach on a spreadsheet. In phase 2, a CRWS model allowed us to discern pipe diameter of two main trunk lines, transporting gas/condensate fluids, using incremental economics.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Modeling & Simulation (0.68)
- Information Technology > Communications > Networks (0.35)
Abstract Annular flow is associated with production from both gas-condensate and geothermal wells. Oil wells also experience it during high-GOR production. The current mechanistic modeling approach entails estimation of film thickness before computing frictional pressure-drop as gas flows past the wavy-liquid film surrounding the pipe wall. This study probes to discern this film thickness and its impact on pressure-drop computation in wellbores producing steam-water, gas-condensate, and gas-oil mixtures. Computational results show that the most likely value this dimensionless liquid-film thickness is less than 0.06 in annular flow. For such thin-film thickness, the computed friction factor is only slightly higher than that estimated with smooth-channel assumption. When the homogeneous model is used to compute pressure gradient by ignoring the wavy liquid film on frictional pressure-drop, good agreement is achieved with field data and those of a mechanistic model. Introduction Annular flow is dominant in gas-condensate and geothermal wells. Oil wells also experience annular flow when high-GOR production occurs after gas breakthrough or when gas-lift is installed. In general, the annular-flow pattern comprises gas core in the middle of the flow string with a thin-liquid film flowing up the pipe wall. Two issues appear to dominate the modeling needs. First, one needs to estimate the liquid entrainment in the gas core, and second, frictional resistance that the gas core experiences when flowing past the wavy-liquid fim. Note that frictional gradient is a very large contributor to the total pressure loss in annular flow and, therefore, has obvious importance. In the past, a few models treated this flow pattern with zero slip between the two phases in the gas core. For instance, the models of Duns and Ros[1] and Aziz et al.,[2] who had essentially adopted the Duns and Ros approach, fall into this category. Subsequently, the method of Hasan and Kabir,[3] based on Wallis'[4] (p. 320) approach, estimates both entrainment and film-friction factor. However, the rigorous method of Ansari et al.[5] is rooted in sound modeling of film thickness, followed by accurate estimation of frictional and hydrostatic heads. The same approach was adopted by Kaya et al.[6] More recently, Gomez et al.[7] proposed a method based on two-fluid approach. The intent of this study is to present an alternative approach to modeling annular flow. We show that the liquid-film thickness is generally very small to be of any consequence when computed with the Ansari et al. model. The main objective is to demonstrate application of a much simpler model with comparable accuracy to a mechanistic model. In fact our recent study[8] on gas-condensate wells shed some light on the possibility of simplified modeling of annular flow.
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- North America > United States > Texas > Permian Basin > Midland Basin > Steen Field (0.89)
- North America > Canada > Northwest Territories > Wallis Field (0.89)
Abstract Gas-condensate reservoirs present unique challenges in terms of assessing liquid content and meeting long-term gas deliverability contracts. When production is initiated, liquid accumulation inevitably reduces a well's productivity index, leading to questions about its ability to sustain the demand rate. Questions also arise whether the liquid content is rich enough to justify cycling lean gas to enhance liquid recovery. This study attempts to address these questions by analyzing data from a large number of West African fields. We used design of experiments (DoE) to derive useful phenomenological correlations for optimal producibility of wells and to discern when a detailed gas-cycling study is warranted. Finite-difference flow simulations in full-compositional mode formed the basis of DoE studies. We observed that sustained well productivity can be had so long one designs completion intervals commensurate with permeability-thickness product and liquid content. In other words, lower offtake rates should be imposed in unfavorable systems to sustain long-term productivity. When lean gas is cycled, incremental recovery over that of depletion case was observed for fluids with 45 to 178 STB/MMscf condensate yields. The producer-injector distance turns out to be the most dominant variable for condensate recovery. A simple correlation is developed to assess the incremental liquid reserves for screening purposes. Introduction Productivity impairment owing to condensate banking near wellbores is caused by decrease in relative permeability to gas. Relative permeability by itself does not describe near-wellbore physics of flow; capillary number effects[1โ4] and reservoir heterogeneity[5] play significant roles. Field observations[4,6โ7] suggest that this impairment manifests in terms of decline in productivity index. The initial liquid content does not appear to have a significant role in this impairment. In other words, even lean-condensate fluids will induce near-wellbore flow problems. However, absolute rock permeability dictates whether the decline in PI materially impacts the demand rate, leading to increased well count to meet contractual obligations. Analytical methods[8,9] are available for predicting well behavior. Although these methods have gained wide acceptance, numerical models in compositional mode are still being used to predict field performance for their greater flexibility. Some of the elements of flexibility include studying optimal completion interval, commingling of production intervals, aquifer influx, and the like. Besides the productivity issue, one other pertinent point a study team often confronts is whether the liquid content in a given reservoir is rich enough to merit probing. Successful gas cycling in condensate-rich reservoirs has been reported in a few field studies.[10โ12] Many of these studies were done with 2D models in the late 1960's and early 1970's showing promise of cycling. However, no simple screening tool exists to evaluate a reservoir's potential. This study has two main objectives. First, we develop simple correlations for predicting gas recovery factor in a depletion mode. In this context, the effects of completion interval and mechanical skin are explored. Estimating length of rate plateau, which, in turn, impacts well count, constituted another objective of this phase of the study. Second, we present a correlation that helps discern incremental liquid reserves owing to lean-gas cycling.
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Carson Creek Field > Beaverhill Lake Formation (0.99)
- Asia > Indonesia > Sumatra > Aceh > North Sumatra Basin > B Block > Arun Field (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)