The Effect of Drainage Radius Interference on the Design of Infill Well in FA Field

ABSTRACT


INTRODUCTION
Field development is a crucial activity in planning future steps to achieve an optimal recovery factor.Adding infill wells is one strategy to maximize reservoir drainage with the goal of increasing the field's recovery factor.Infill wells are usually added to areas that still have parts of the reservoir that are not fully depleted.The planning method described in this paper involves the use of reservoir simulation.Reservoir simulation involves creating a model of the reservoir that resembles the actual conditions.The parameters in the model match the actual reservoir parameters.With this model, development planning can be carried out with various scenarios without having to conduct direct exploration in the actual reservoir.This approach helps reduce field development costs.The results of the reservoir simulation include various development scenarios that provide cumulative production data to determine the optimal recovery factor.The purpose of reservoir simulation is to create an artificial reservoir model that is similar to the actual reservoir in the field.With the help of reservoir simulation, development planning can be done more easily and efficiently, resulting in development scenarios that are then practically implemented.The optimization of infill well addition in development planning aims to achieve optimal results from the addition of infill wells, measured by production effectiveness, production efficiency, and economic aspects.Several studies show different approaches to adding infill wells and their impacts in the field.Ahmed, T. (2010) in his research on optimizing infill drilling in old oil fields using reservoir simulation methods concluded that infill drilling can increase oil recovery by up to 15% in old oil fields with heterogeneous reservoirs.The reservoir simulation method used in this study allows for the identification of areas with high oil saturation, which are the primary targets for placing infill wells.By using this approach, oil production can be optimized without excessive new well drilling, significantly reducing operational costs.Furthermore, this study highlights the importance of detailed reservoir characteristic analysis to ensure optimal infill well locations, which also helps minimize overlap with existing wells.Adding infill wells in heterogeneous reservoirs can increase cumulative production by up to 20%, especially when wells are placed in areas with high oil saturation.This research emphasizes the importance of advanced simulation technology in designing effective infill drilling strategies.By mapping the oil saturation distribution in detail, operators can determine the best locations for infill wells, thereby reducing resource wastage and maximizing oil extraction, according to Chen and Liu (2014) in their research on the simulation of infill drilling impact on oil recovery using numerical reservoir simulation methods.Sajjad et al., (2024) in their research on efforts to increase oil production through infill drilling aimed to provide a comprehensive overview for determining the optimal infill well locations using decline curve analysis and volumetric methods to estimate the required number of infill wells.First, using empirical methodology that considers the number and distance between production wells and infill wells, and second, using simulation and numerical optimization methods that evaluate the potential of production wells with planned infill wells.The results of this study indicate that infill drilling has proven to be an effective method for increasing reserves Am.J. Innov.Sci.Eng. 3(2) 20-29, 2024 in several reservoirs, and reservoir homogeneity can be used to predict additional reserves resulting from infill drilling.Most oil fields in Western Siberia are marginal, making horizontal infill drilling in these fields difficult.Conventional drilling often fails due to mud loss issues, significantly increasing the need for drilling fluids.Using existing geological data, an oil-in-water emulsion system was developed for drilling fluids to avoid formation damage and blind drilling in low/depleted reservoir pressure conditions (Sokovnin et al., 2015).Infill drilling plays an important role in the development of oil and gas fields.Planning infill drilling in marginal reservoirs poses significant success challenges.Conventional evaluation approaches require considerable time and cost.Optimal planning simulation can support the success of infill drilling using sequential inversion algorithms and historical well matching.This approach can complete infill drilling planning more quickly and cost-effectively (Cheng et al. 2006).Increasing production in old fields is very important.One way to accelerate and enhance field production is through infill drilling.Optimizing well spacing is crucial to reduce interference with existing wells and increase the well drainage radius.It is estimated based on correlation methods and adjacent wells, and the Proved Developed Non-Producing (PDNP) reserve polygon map can serve as the basis for determining infill wells.The results of this analysis can estimate the well drainage radius behind the casing based on the correlation method and adjacent wells (Amal et al., 2021).Determining infill well locations is a complex challenge that takes considerable time.Calculating remaining oil saturation to evaluate remaining reserves is deemed less effective.Researchers comprehensively consider other factors in determining remaining reserves by developing a quantitative evaluation method with a mathematical model to determine the optimal infill well location by optimizing the coordinates, depth, and inclination of the well.This method successfully assesses remaining reserves comprehensively and characterizes reservoir conditions.Thus, effectively determining the range of infill well locations for reservoir development (Liu et al., 2024).Adding infill wells is a quick and significant effort to increase field production.The analysis is carried out in an integrated manner by determining the rock type in the target reservoir and determining the Hydrocarbon Pore Volume (HCPV).Infill well locations are determined based on good HCPV values, followed by production forecasting using the decline curve method, and then economic analysis is conducted to determine the success rate of the infill well plan.The rock type classification in the studied formation (Duri Formation) uses the Hydraulic Flow Unit (HFU) method.

MATERIALS AND METHODS
The research flowchart can be seen in Figure 1 The OOIP (Original Oil in Place) of a field also needs to be calculated to determine whether a field is still feasible for development by evaluating the advantages and disadvantages of each field development option.The OOIP of a field can be calculated using the following formula: (1) The calculation of the Recovery Factor at this stage aims to determine the maximum reserves obtained after infill drilling.The RF value is calculated using the following equation: (2) The determination of the Estimated Ultimate Recovery (EUR) aims to ascertain the maximum reserves that can be extracted during the primary recovery stage.The Estimated Ultimate Recovery can be determined using the following equation: EUR = RF x OOIP (3) Remaining reserves are the residual reserves that have not yet been produced.The remaining reserves in the FA field can be calculated as follows: RR = EUR -NP (4) The accuracy of a reservoir model in reflecting the actual reservoir greatly depends on the completeness and processing of available data, particularly in terms of reservoir data processing [9].The goal of reservoir data processing is to optimize limited information through detailed analysis and processing, resulting in an accurate model that represents the actual reservoir [10].Reservoir data processing includes: Determining the rock area, Processing Special Core Analysis (SCAL data: core sample data, endpoint data, average data region, normalization, and reconstruction of relative permeability curves), Processing PVT data, and Processing production history data.Before starting the history match stage, the reservoir model begins with initialization to check and establish the initial equilibrium conditions of the reservoir and determine the initial volume in the reservoir.The  (Ahmed, 2006).History matching is conducted to test the accuracy of the reservoir model's production against actual field data.In this process, the key parameter is the production flow rate, meaning the production flow rate entered into the simulation model must match the actual production flow rate from historical field data (Dake, 1978).Additionally, various factors such as oil production rate, water production rate, pressure, gas-oil ratio, and water cut percentage must be adjusted to match the actual field data(Ding, (1995): Infill Well Point Plan of FA Field Determining the locations for infill wells in the field begins with identifying the rock regions.For Field "FA," rock regions are determined using permeability analysis plots imported from the simulator along with sample numbers.The tabulation of rock region distribution for Field "FA" can be seen in Table 1.Based on the analysis of the iso-saturation map, it can be concluded that at depths ranging from 4541.0 ft to 4550.3 ft, the oil saturation ranges from 40% to 80% until the end of forecasting, making it suitable for planning infill wells at two pre-determined points with saturations between 40% and 80%.By analyzing the drainage radius of each well, production can be maximized.In Field FA, there are 13 development wells already drilled, with additional points capable of development through infill well planning to accelerate field production.Based on this bubble map analysis, two potential infill well planning points have been identified to expedite field production.These two infill well planning points can be seen in the following Figure 4.
Based on the Bubble Map (Figure 4), it can be observed that there are two locations with oil saturation values between 40-60%, suitable for planning infill wells to expedite field production.At the INF-1 infill well location, it is noted that there are four surrounding wells (A-1, A-3, A-5, and A-7) with overlapping drainage radius, theoretically affecting the production of each influenced well.Meanwhile, at the INF-2 infill well location, there are only three surrounding wells with overlapping drainage radius (A-10 and A-12).These two locations are therefore candidates for infill well sites.

The Effect of Drainage Radius Interference on Actual Well Production Decline
The initial step before conducting simulations in the "FA" Reservoir Field involves preparing the necessary data for the simulation purposes.The processed RCAL data will generate rock grouping based on permeability uniformity, known as "rock regions."Determining rock regions is done to understand the distribution of reservoir properties.
The initialization process involves a reassessment of the data input into the simulator, which includes adjusting simulation data to match the initial conditions or aligning reservoir simulation calculations with actual data.In this context, matching is done to ensure that the simulated Oil in Place (OOIP) aligns with volumetric calculations.Initialization aims to reconcile volumetric OOIP with simulation results.Based on volumetric OOIP calculations yielding 12.73 MMSTB and simulated results also at 12.73 MMSTB, the % error between simulation and volumetric results is 0.0014%, well below the maximum % error tolerance of 1%, indicating alignment between simulation and volumetric results.History matching is the process of aligning simulation models with actual conditions based on measurable parameter data over a period of time, involving adjustments to dynamic parameters to achieve alignment.
To match production history, adjustments are made to SCAL data to align oil, gas, and water production rates by modifying parameters such as Kro, Krg, and Krw.History matching for the "FA" field was conducted from January 1, 2007, to January 1, 2024, by plotting cumulative production rate parameters for oil, water, and gas in the "FA" field.The results of the history matching for the "FA" field can be seen in the following Table 2. Based on Table 2, it can be observed that there is a decrease in production rates each year in the field, indicating a need for further optimization and acceleration of production to maximize yields from the field.According to the bubble map analysis, considering oil per unit area, infill well placement is determined.Based on the bubble map, the planned locations for infill wells affect the drainage radius of surrounding wells, including wells A-1, A-3, A-5, A-7, A-10, and A-12. Figure 5 shows the results of history matching for oil and water rates in Field "FA".Based on Figure 5, it can be concluded that the simulation results show a significant decline in annual oil and water production rates during the first five years in Field "FA".Therefore, optimization is necessary to increase the production rates in the field by implementing infill well planning in Field "FA".Before engaging in development scenarios, the initial step is to run the basecase.In this scenario, no development activities are carried out (no future action).During this phase, the simulator runs for 10 years to evaluate the field production performance without any development plans.

Scenario 1 (Basecase)
The basecase predictions will serve as a benchmark for comparison in planning subsequent scenarios, allowing us to determine the expected increase in oil recovery and Recovery Factor for Field "FA" without any development scenarios.Figure 6 shows the plot results of the oil rate basecase up to the end of January 2024, illustrating the production performance until the end of the prediction period without implementing any optimization activities.
Based on the graph in Figure 6, the cumulative oil production from the total of 7 observation wells amounts to 3.58 MMSTB.Therefore, the recovery factor is calculated to be 28.09%.

Scenario 2 (Baseline + 1 Infill Well with 4 Drainage Radius Interferences)
In this scenario, one infill well planning was conducted based on the analysis of the iso-saturation map to observe the oil saturation at the designated infill point.Below is the bubble map depicting the scenario of planning one infill well affecting four surrounding wells.The drainage interference radius of the planned infill well can be seen in the following image.Based on the bubble map, planning one infill well INF-1 affects four surrounding wells: A-1, A-3, A-5, and A-7.Below is the tabulation of oil rates obtained after implementing the planning of one infill well to maximize oil recovery.The graph depicting the Oil and Water Rate Acquisition in Scenario 2 for Field "FA" can be seen in Figure 8 shows.
Based on Figure 8, it is evident that there is a decline in oil and water production rates over time as the wells are produced.In the first year after implementing the infill well planning, the cumulative oil production totals 3.77 MMSTB, resulting in a recovery factor of 29.6% for Field "FA" in Scenario 2 with one infill well planning and four drainage interference radius.For a comparison of oil production rates before and after implementing one infill well planning, refer to Figure 9 below.Based on the bubble map, with planning for two infill wells INF-1 and INF-2, it can be seen that they affect six surrounding wells: wells A-1, A-3, A-5, A-7, A-10, and A-12.The graph depicting the Oil and Water Rate Acquisition in Scenario 4 for Field "FA" can be seen in Figure 14 below.Based on Figure 14, it can be observed that there is a decline in oil and water production rates over time as the wells are produced.In the first year after implementing the infill well planning, the cumulative oil production totals 3.94 MMSTB, resulting in a recovery factor of 30.92% for Field "FA" with planning for two infill wells and six drainage interference radius.For a comparison of oil production rates before and after implementing the scenario, please refer to Figure 15 below.Based on Figure 15, it can be concluded that planning for two infill wells with six drainage interference radii increases the highest oil production rate in the field by bbl/day, from 430 bbl/day to 510 bbl/day, in the first year after implementing the two infill well planning.Planning one infill well, INF-2, with two affected wells resulted in a production decline of 6.37 BOPD with an additional production of 44 BOPD.From both scenarios, it is evident that the more overlapping areas caused by infill well planning, the more the actual production rate of surrounding wells will decrease.In the base case scenario without optimization, the recovery factor is only 28.09%.With planning for one infill well and four

Figure 1 :
Figure 1: Research Methodology Flowchart Visc.Oi = 16.46 cp Visc.Wi = 0.89 cp Pb = 3400 psi Pa = 150 psi By incorporating the known data into the equation above, the Recovery Factor (RF) for Field FA is found to be 65%.The determination of the Estimated Ultimate Recovery (EUR) aims to identify the maximum reserves that can be extracted during the primary recovery phase.The Estimated Ultimate Recovery can be determined using the following equation: RF = 65% OOIP = 12.73 MMSTB EUR = RF x OOIP = 8.27 MMSTB Remaining reserves are the leftover reserves that have not yet been produced.The remaining reserves in Field FA can be calculated using the following formula: Diketahui data : EUR = 8.27 MMSTB NP = 2.87 MMSTB RF = NP/OOIP x 100% = 2.87 MMSTB/12.73MMSTB x 100% = 22.55% RR = EUR -NP RR = 5.41 MMSTB RFcurrent = NP /OOIP x 100% = 2.87 MMSTB /12.73 MMSTB x 100% = 22.55% radius, ft Np = Cumulative oil production, stb Boi = Oil formation volume factor, stb/bbl h = Net pay thickness, ft Φ = Porosity, fraction Swi = Initial water saturation, fractionRESULTS AND DISCUSSIONRemaining Reserve of FA FieldThe calculation of the Recovery Factor (RF) in Field FA aims to determine the maximum reserves obtained after infill drilling.The calculation of the RF value is performed using the following equation:

Figure 5 :
Figure 5: Oil and Water Production Rate History Graphic

Figure 9 :
Figure 9: Comparison of Oil Production Rates Before and After Adding 1 Infill Well and 4 Drainage Radius Interference

Figure 13 :
Figure 13: Drainage Radius Interference with 2 Infill Well Plan

Figure 14 :Figure 15 :
Figure 14: Oil and Water Production Rates Scenario 4

Table 1 :
Rock Region Distribution "FA" Field No.From Table1, it can be seen that there are 2 regions, namely region 1 and 2. Region 1 represents an area with an average permeability distribution below 8.7596 mD, while region 2 has an average permeability value distribution of 30.4179 mD, and region 3 has an average permeability distribution value of 59.1931 MD.Figure3below shows the graphical representation of rock region or rock type based on rock permeability distribution.