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Gas-condensate reservoirs exhibit complex multi-phase flow behavior below saturation pressures resulting from compositional variations. Below saturation pressure, condensate starts dropping-out within the reservoir and eventually develops a bank in the near wellbore region; effectively reducing the relative permeability to gas flow. As a result, production of main phase (gas) decreases with development of this bank while condensate is left behind in the reservoir. Since, reserves estimation is a part of the development plans of all fields, it is important to account for condensate drop-out while deriving a condensate recovery factor during the initial stage of the life of a reservoir. Otherwise a high condensate recovery factor (if assumed equal to or close to that of gas phase) might result in unrealistic optimistic cash flow projections. Given the importance of condensate recovery factor estimation, a data driven analytical method has been developed and will be discussed in detail in this paper; while overcoming the limitation of available data for green fields. An analytical methodology has been developed for calculation of condensate recovery factor in gas condensate reservoirs in the absence of a constant volume depletion (CVD) study laboratory report. The method uses either a linear or a non-linear correlation-based relationship between condensate drop-out and pressure. This enables derivation of an equation for calculation of condensate recovery factor while incorporating critical parameters such as dew point pressure, initial and abandonment pressures and condensate gas ratios (CGRs) at initial and abandonment conditions. A stepwise procedure will be described to derive all these parameters with limited available data in the early stages of the life of reservoir. The methodology adopted was applied to more than 30 green and brown gas condensate fields with varying CGR's. For a few mature fields, results derived from the proposed methodology were compared with recovery factors estimated from numerical simulation models. Results of both approaches appeared to be in a good agreement with an accuracy of +/−10%. As expected, recovery factors derived assuming a simple linear decline of CGR with pressure were less accurate than those calculated using a non-linear correlation-based decline of CGR with pressure. For richer gas condensate fields with high compressibility factors, the z-factor was incorporated with pressure in the form of P/z, which resulted in a more accurate estimation of recovery factors. A comparison has been made where recovery factors are estimated from the described approach and the other reference approaches; and conclusions drawn from these comparisons are discussed. Various approaches have been used historically for condensate recovery factor estimation while relying heavily on a good amount of data available. A simple analytical approach has been discussed which mitigates the impact of poor data availability especially in green fields and yet can yield useable condensate recovery factor calculations. This enables better reserve estimation of condensates in gas condensate reservoirs in early stages of life of reservoirs, while leading to efficient and realistic field development plans in futures.

The subject Gas Field is located in the Sulaiman Fold Belt (SFB) in Pakistan. A realistic 3D static model was constructed for the challenging multiple reservoirs in the Field which included both clastics and carbonates. Four main reservoir horizons were modeled. The steps involved in the Reservoir engineering analyses were: analyze PVT, well test, Static Pressure Data, and Core. The static pressure analysis helped define hydraulic compartmentalization in the field. WHFP measurements were not available in the desired accuracy and density. A surface network model was used with plant inlet pressure as the primary constraint in order to obtain the required information. Satellite based elevation information was used to establish an accurate model with respect to pressure drop due to liquid hold up in pipelines. The History Match in the field was performed on a Zone by Zone basis. In the absence of a 3D seismic cube, many of the faults in the field could not be interpreted, yet their presence was predicted by a closely matching Sand Box Model. This was an important clue which led to a useful approach regarding the location of simulation faults distributed in the entire field. An innovative approach was used in order to calibrate the size of sand lenses in one of the zones. The final step was the forecasting and development of Optimal Scenario using Economic analysis. Many scenarios were tested, and the optimal scenario was identified. Maximum use was made of existing wellbores through re-completion, and new drilling was minimized. Furthermore, the impact of increasing the currently low Gas Price was tested. It was concluded that doubling of the gas price of the field would increase the NPV 3 times delay abandonment by 6 years.

As an increasing percentage of the world’s production comes from mature fields, there is a growing need for production enhancement techniques that are both rapid and easy to use for the practicing production engineers. For mature waterfloods, the ln(WOR) versus Np plot enables rapid well screening on the basis of incremental recovery factor, where WOR is the producing Water Oil Ratio and Np is the cumulative oil production. Published in-depth information on application of this tool is sparse. Yet, this is often the only tool available to the production engineer for evaluating development options, where a history-matched simulation model has not been maintained. In this paper, the theoretical basis for the use of the ln(WOR) versus Np is reviewed and studied, and is used to arrive at practical guidelines for interpreting production data. Its applicability as a forecasting tool to single-layered and multilayered clastic, waterflooded reservoirs of varying heterogeneity is demonstrated. Numerical simulation models then predict the behaviour of this plot for a wide range of heterogeneities. Production data is then analysed to show the applications of the theory for multilayered reservoirs. The ln(WOR) versus Np plots are analysed, and the impact of various factors is observed. The authors also demonstrate that, where applicable, this plot is the preferred decline curve for the following reasons: –Ln(WOR) versus Np does not require any pressure data; only surface well test production history is required.–It can be assumed that the ln(WOR) versus Np function is an approximate function of the reservoir only, and is decoupled from the outflow and facility constraints. This is especially useful when comparing artificial lift and drawdown strategies.–It is a decline curve model that provides a forecast of water cut, which is indispensable on waterflood projects.

In many oil wells, production is commingled from several layers. In such environments, understanding the properties of individual layers is essential to reservoir surveillance and production optimization. The inflow properties that typically require measuring are productivity index (PI), water cut, and static reservoir pressure. These measurements have traditionally been taken with wireline-conveyed production logging tools (PLT); however, in many wells and operating environments, completion and logistic considerations make running these tools difficult or even impossible. In those instances, an alternative is required. This paper presents a new procedure based on multirate testing in combination with distributed temperature sensors (DTS), an electric submersible pump (ESP) fitted with a gauge in the commingled fluid stream, and conventional surface testing. The additional test rates provide sufficient equations to resolve all the unknowns, whereas the DTS provides essential information such as which layers are producing and which are taking fluid, as well as a mass flowrate tool for measuring the flow rate of each layer. The procedure requires varying well flow rates over a range sufficient to ensure that all layers are producing, which in many cases requires an ESP to provide sufficient drawdown to overcome crossflow as well as a variable-speed drive to establish the test rates. The theoretical basis for the protocol is described, and an example is detailed to demonstrate the validity and robustness of the method for determining inflow properties. Finally, theoretical and practical guidelines are provided to demonstrate how the test procedure is affected by fiber-optic resolution, fluid properties, and the geothermal gradient. For wells equipped with forms of artificial lift such as ESPs, beam pumps, and progressive cavity pumps (PCPs), running PLTs is often not possible because of the completion obstruction. In such cases, this new DTS-enabled procedure has the potential of becoming the PLT substitute of the future.

A study was carried out to forecast the productivity of a hydraulically fractured well in a retrograde gas-condensate sandstone reservoir using a numerical model. The fracture was explicitly modeled as a set of high-conductivity cells. At the gas velocities normally encountered in hydraulic fracture proppant packs, non-Darcy pressure drops dominate, and the apparent proppant permeability is one or two orders of magnitude lower than the Darcy permeability measured at single phase low-rate conditions. This is particularly true if a liquid phase is also flowing. The apparent permeability of the proppant is a function of: Gas velocity (hence: rate and flowing pressure)Ratio of free liquid rate to gas rateStress on the proppantType of proppant Thus, apparent proppant permeability will vary with distance from the wellbore, increasing towards the tip of the fracture where liquid ratio and velocity are lower. This variation of permeability was explicitly modeled in the proppant pack by dividing it into segments and calculating the permeability in each segment. As a result of this modeling, the impact of increased fracture length on productivity was found to be more significant than in simpler modeling where one permeability value is used for the entire proppant pack. The variation of apparent proppant permeability along the length of the fracture and its impact on well productivity are discussed in this paper. A comparison of predicted well productivity is also made with the use of a constant permeability value for the proppant in numerical and analytic simulators. We will show that using a constant proppant permeability value results in an estimate of optimal fracture length that is too short.

An innovation in the methodology of conducting drillstem tests (DSTs) in tight gas reservoirs is presented, along with a simplification in the interpretation of the data obtained. DSTs in tight gas reservoirs are a problem because the flow rates are often too low to be measured by conventional equipment. In these cases a normal flowing and buildup test should be followed by a closed chamber test to produce usable estimates of flow rates at various times during the preceding normal flowing and buildup test. This estimated flow rate can then be used to interpret the buildup after the flow testing, which is produced by shutting the downhole valve. In the interpretation of DST data from a tight gas reservoir, factors such as the following have to be considered: 1) large variation of gas properties resulting from the large pressure range involved1,2; 2) flow rate duration that can be many times shorter than the buildup period3,4,5,6; 3) varying flow rate during the flowing part of the test; and 4) impact of boundaries and heterogeneities. We show that a simple interpretation approach with constant flow rates and pseudo-pressure yields results that are within the intrinsic accuracy limits expected from such a test. The benefits to the field interpreter of using the methods presented are 1) operational flexibility: if the well flows strongly enough the rate may be measured at surface; if it does not, a closed chamber test can be added; 2) the benefit of the deeper radius of investigation of a test flowing at surface is retained; 3) field interpretation is simpler whether surface measurement is possible or not.