EVALUATION OF AKPET GT9 GAS CONDENSATE RESERVOIR PERFORMANCE

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INTRODUCTION
In most parts of the world today, there are many gas condensate reservoirs and each with a unique composition.
This implies that the ability of a Reservoir Engineer to predict the behavior of gas condensate reservoirs depends solely on his/her ability to predict the flow characteristics of the fluids in the reservoir. Thus, the main concern of the engineer in carrying out study on the reservoir is to adequately simulate the reservoir with the minimum effort.
Gas condensate reservoir can be defined as a low-density mixture of hydrocarbon liquids that are present as gaseous components in the raw natural gas produced from many natural gas fields. It condenses out of the raw gas if the temperature is reduced to below the hydrocarbon dew point temperature of the raw gas. However, gas condensate generally has a specific gravity ranging from 0.5 to 0.8, and is composed of hydrocarbons such as propane, butane, pentane, hexane and other hydrocarbons which exist as liquids at ambient temperatures and may contain impurities such as Hydrogen sulfide (H2S), Mercaptans, Carbon dioxide (CO2) etc.
In the production of a gas condensate field, gas is mostly produced with some liquid dropout as the pressure drops below dew point pressure; occurring mostly in the separator and can still be produced in the wellbore which ultimately leads to a restriction in the flow of gas. The temperature and pressure may change once the reservoir fluids enter into the wellbore which causes liquid to drop out within the wellbore and if the gas having the larger fraction does not have enough energy to lift the drop out liquid to the surface, a fallback in the wellbore occurs or liquid loading. If this continuous, the percentage of the liquid will increase and may eventually restrict the gas production. This challenge can be adequately handled with artificial lift technologies such as gas lift, but this will not be considered in this paper. Figure 1, presents a retrograde gas field whose temperature is greater than the critical point temperature.
The curve lines indicate the phase changes as the fluid flows from the reservoir (green vertical line), cools as it goes up through the wellbore and then into the separator. Point A in Figure 1 represents percent increase in liquid with the retrograde condensate field and then decreases with further pressure declines. Thus, condensation and then vaporization occurs which help in the further recovery of liquids.  Thomas et al. (2009) stated that to get an insight into gas condensate reservoir, appropriate in situ fluid characterization and performing relevant flow test is key to the success of its performance prediction. They quantified the differences caused by compositional path in API gravity of liquids in solution to be as much as 10 degrees and 110 Daltons differences in molecular weight. It was also observed that the critical condensate saturation was less sensitive to interfacial tension but the end-point saturations are sensitive to interfacial tension. Babalola et al. (2009a) stated that original and best modified black oil models are limited and thus, not adequate for modelling gas condensate reservoirs as a result of the deterministic nature of evaluation. A stochastic gas condensate model is required to match the adequacy and serve optimal recovery techniques. Babalola et al. (2009b) transformed the partial differential equation of deterministic method of mass balance into a stochastic technique of Feynman-Kac differential equation by employing some simple and safe assumptions. The stochastic parameter k was determined and found to be zero. Ahmadi et al. (2014) posits that there is a significant decrease in the well productivity as a result of a sharp drop in the effective gas permeability when there is a decrease in the bottom-hole pressure below its dew point due to condensate bank around the gas condensate well. Hence, to adequately predict the performance of the reservoir, an engineer requires a thorough understanding of the fluid behavior. The aim of the work was to compare the various simulation techniques of gas condensate reservoir, to determine the most accurate with cases below and above the gas condensate dew-point. Olaberinjo and Omole (2004) presented a systematic and inexpensive compositional approach that is based on Cho et al. (1985) correlation for calculating gas condensate reservoirs pressure depletion performance with consideration to the properties of liquid and vapor phase with possible presence of impurities such as CO2, H2S and S. Gringarten and Al-Lamki (2000) showed that three regions are created with different liquid saturations when the pressure of the reservoir in the vicinity of a well drop, a retrograde condensation occurs below the dew point pressure. Considering the point closer to the well where the liquid saturation reaches a critical value, an inner region forms and the effluent travels as two-phase flow with constant composition. There is an immobile intermediate region where the liquid is less than the critical condensate saturation. This region is characterized by a rapid increase in liquid saturation and a corresponding decrease in gas relative permeability. Away from the well, an outer region has the initial liquid saturation next to the intermediate region.

STATEMENT OF PROBLEM
The accurate determination of oil and gas initially in place and prediction of the future reservoir performance from any of the reserve estimation methods are of vital importance to the oil and gas industry, also an accurate and reliable production data directly impacts the accuracy of the in-place volume estimate and future performance.
Thus, there is a challenge faced in gas condensate fields and how a company should optimize the development of a gas-condensate field, when depletion leaves valuable condensate fluids in a reservoir which builds up near a well because of the drawdown below the dew point pressure, ultimately restricting the flow of gas. The problem of liquid loading can be effectively handled by installing a gas lift or pumping technologies but this is beyond the scope of this study.

STUDY OBJECTIVES
This study is primarily aimed at the evaluation of a gas condensate reservoir performance of Akpet Reservoir in the Niger Delta region with material balance tool (MBal) without having to run dynamic/numerical simulations. This is important for the optimization of the production strategy of gas-condensate reservoirs, reducing the impact of condensate banking, and improving the ultimate gas and condensate recovery. The objectives were to:  estimate the Hydrocarbon-in-Place  evaluate the available drive mechanisms and the strength of the aquifer  determine the most likely aquifer model and properties  determine the probable limits of the reservoir The in-place volumes acquired from the study are subject to validation by static and dynamic simulation.

MATHEMATICAL MODEL
The calculation of the original oil and gas in place are given in equations 1 -7: The derivation of the general material balance equation for a wet gas reservoir is given by equations 10 -17:

METHODOLOGY
The Material Balance Analysis tool, MBAL, of Petroleum Experts Limited was used for the analysis. The program uses a conceptual model of the reservoir to predict the reservoir behavior and reserves based on the effects of fluids production from the reservoir. Detailed PVT models can be constructed for oils (both black oil and compositional), gases and condensates. Furthermore, predictions can be made with or without well models and the use of relative permeability to predict the number of associated phase productions. MBAL can also be tied into GAP for integrated production modeling studies, providing an accurate and fast reservoir model as long as the assumptions of material balance are valid for the real situation to be modeled. The reservoir pressure, PVT and production data, after careful review, served as input data into the MBAL program. Available geological maps and petro-physical data aided the estimation of the aerial extent of the reservoir and the aquifer.

PROCEDURE
The Havlena-Odeh and the F/Et vs. We/Et straight line plots of the graphical method incorporating various radial aquifer models were used to evaluate the aquifer properties, match the reservoir pressure and determine the gas initially-in-place (GIIP). The accuracy of the results was validated with the history match of the model's pressure and production. The analysis procedure is as follows:  Pressure and production data is entered on a Tank basis.
 The matching facility in MBAL is used to adjust the empirical fluid property correlations to fit measured PVT laboratory data. Correlations are modified using a non-linear regression technique to best fit the measured data.
 The graphical method plot is used to visually determine the different Reservoir and Aquifer parameters.
The Havlena -Odeh and the F/Et vs. We/Et straight-line plots of the graphical method were used to visually observe and determine the appropriate aquifer model and parameters.
 The non-linear regression engine of the analytical method is used in estimating the unknown reservoir and aquifer parameters and to fine-tune the pressure and production match. This is done for various aquifer models and their standard deviations from the actual field data are compared  The accuracy of the model is validated by history matching the field pressure and production data with the simulation data.

DATA
Akpet GT 9 reservoir was analyzed as a gas condensate reservoir. The pressure and production data used in the analysis was provided by an Oil and Gas Company whose identity cannot be revealed due to data integrity issues.
The production history spans a period of 23 years (February 1978-December 2001. The PVT, the reservoir (tank) and relative permeability data used in the analysis are shown in Table 1, 2 and 3 respectively.

 Unknown aquifer characteristics and properties
Inferences from the Material Balance analysis of the Akpet GT 9 reservoir are as follows:  The GIIP is 231.934 Bscf..
 The most likely aquifer model is the Hurst-Van Everdingen -Dake radial aquifer.
 The reservoir is supported by a combined drive of water influx and fluid expansion as shown below (Figure 4).
The summary of the aquifer parameters used in the Material Balance calculations and the source of each data are depicted in Table 4. The parameters used to obtain the history match compares favorably with the expected values. The plots generated from the more likely case model are shown in Figures 3, 4, 5 and 6 respectively.
The Gas initially in place (GIIP) for the reservoir was given from volumetric technique as 231.68 Bscf and after the history match performance of the production history ( Figure 3); the result from the graphical method using straight line approach incorporated into the Mbal software gave GIIP value of 231.934 Bscf. Thus, since this value agrees with the one calculated with the volumetric method at the initial stage of the reservoir; it can, therefore, be used to predict further the performance of the Akpet GT 9 reservoir to abandonment pressure.

CONCLUSIONS CONCLUSIONS
The following conclusion where drawn from the observations of the results from the study:  The result obtained with MBal on the analysis of Akpet GT 9 reservoir show that the most likely aquifer model is the Hurst-Van Everdingen -Dake radial aquifer and the reservoir is supported by a combined drive of water influx and fluid expansion.
 The modified form of the material balance equation could be used to predict changes in produce fluid compositions as a function of production, and to accurately predict the gas and gaseous equivalence initially in place, recovery factor and hence reserves.
 The MBAL approach proves to be sufficient for modeling gas condensate behavior below the dew point.
The MBAL simulation model can adequately simulate the depletion and water influx processes for gas condensate reservoirs. Using the MBAL approach, instead of a fully compositional approach, may result in significant time saving especially in full-field simulation studies. Partial pressure maintenance operational scheme should be carried out to produce more condensate liquids to minimize condensate fluid loss to the reservoir.
 History Matching must not be achieved at the expense of parameter modifications that are physically and/or geologically wrong. Moreover, the prediction cases should not exceed capabilities of the model and must be consistent with field practices.