Forecasting in the Eagle Ford Shale
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Authors Saputra, Wardana;Patzek, Tadeusz;Torres-Verdín, Carlos Citation Saputra, W., Patzek, T. W., & Torres-Verdin, C. (2023).
Physics-based and Data-driven Production Forecasting in Eagle Ford Shale. Proceedings of the 11th Unconventional Resources Technology Conference. https://doi.org/10.15530/
urtec-2023-3858983 Eprint version Post-print
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Physics-Based and Data-Driven Production Forecasting in the Eagle Ford Shale
Wardana Saputra*1, Tadeusz Patzek2, Carlos Torres-Verdín1, 1The University of Texas at Austin, 2King Abdullah University of Science and Technology.
Copyright 2023, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2023-3858983
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA, 13-15 June 2023.
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Abstract
We develop and successfully verify a reliable method that matches the fieldwide oil and gas production from all horizontal hydrofractured wells in the Eagle Ford Shale and calculate the play-wide Estimated Ultimate Recovery (EUR). Unlike purely empirical industry-standard forecasting methods, our approach relies on the physics of hydrocarbon flow in a hydrofractured shale geometry and captures the probabilistic uncertainty of shale geology and well productivity.
For this study, the Eagle Ford play is divided into 24 spatiotemporal well cohorts based on shale geology, fluid composition, and completion date. For each well cohort, we fit the distribution of annual production via Generalized Extreme Value statistics. Expected values are then used to construct historical well prototypes. Next, we extrapolate these well prototypes for up to two more decades, using a physical scaling method that accounts for variations in fluid composition across the Eagle Ford Shale. The resulting well prototypes provide robust history matches and predictions of total field production. Finally, to estimate the play-wide EUR, we calculate the well infill potentials for each subregion of Eagle Ford, then we assign the well prototype to each of the potential wells.
Based on fluid composition and shale geology, we first mapped all Eagle Ford wells into eight spatial cohorts. To capture the advancement of completion technologies over time, we further divided the well cohorts into three completion date intervals. The total 25,707 existing wells in the Eagle Ford will ultimately yield 2.53 Gbbl of crude oil, 2.79 Gbbl of natural gas liquid (NGL), and 25.67 Tscf of natural gas by 2035.
We found that there are 50,115 potential wells that can be drilled across 18,665 sq. mi of Eagle Ford play.
With future drilling programs, there will be additional 8.60 Gbbl of crude oil, 2.42 Gbbl of NGL, and 63.7 Tscf of natural gas by 2065.
To our knowledge, this project is the first successful attempt to evaluate the play-wide ultimate recovery of the Eagle Ford shale combining physical scaling, generalized extreme value statistics, play geology, and realistic future drilling programs. We also develop a new reserve-assessment method in shales that considers not only the geology of the shale play geology, but also the production dynamics with uncertainty quantifications. In our opinion, this hybrid, data-driven, and physics-based approach is the future of
production forecasting and reserves estimation in all shale plays which also provides an objective way of avoiding estimates that are unrealistically low or high.
Introduction
Eagle Ford Shale play is currently the third largest petroleum liquid producer in the U.S. spanning over 18,665 square miles in South Texas. The shale formation was deposited 145 million years ago during the Cretaceous period consisting of organic-rich mudstone and chalk minerals (Donaldson et al., 2013). Like other shale formations, Eagle Ford has an extremely low permeability (50 – 1500 nanodarcy) that requires hydraulic fracturing technology to yield commercial production rates. The first Eagle Ford well was drilled and completed in 2008 in La Salle County by Petrohawk Energy Corporation (Cusack et al., 2010). Today, there exist 25,707 Eagle Ford wells in 30 Texas counties, cumulatively producing 2.04 Gbbl of crude oil, 2.28 Gbbl of natural gas liquid (NGL), and 19.9 Tscf of natural gas. The wide variation in Eagle Ford produced fluids is due to the fact that the Eagle Ford formation dips steeply toward the Gulf of Mexico from 4,000 to 14,000 feet below sea level which affects hydrocarbon maturity (Inamdar et al., 2010).
Resource assessment in Eagle Ford Shale play is a challenging task due the high complexity of reservoir fluids and geological structures. The U.S. Geological Survey (USGS) used a geology-based resource assessment method dividing the play into seven assessment units (AUs) of oil and gas in Eagle Ford Marl and Cenomanian-Turonian Mudstone formations (Whidden et al., 2018). On the other hand, the U.S.
Energy Information and Administration (EIA) divided the play into three zones: dry, wet, and oil zone, and used hyperbolic decline curve analysis to estimate ultimate recovery (EUR) per well (EIA, 2023). (Gong et al., 2013) further divided the play into eight production regions of different reservoir fluids and used probabilistic decline curve analysis to estimate EUR.
A purely geology-based assessment may not be able to capture the production dynamics of ever-changing shale development. Meanwhile, purely empirical industry-standard forecasting methods, i.e. decline curve analysis (Aarps, 1945) tend to overestimate EUR. In this study, we propose a hybrid physics-based and data-driven method to calculate the play-wide EUR of Eagle Ford Shale play. Our approach relies on the physics of hydrocarbon flow in a hydrofractured shale geometry and captures the probabilistic uncertainty of shale geology and productivity of complex Eagle Ford wells. This method has been successfully applied to the Barnett (Patzek et al., 2019), Bakken (Saputra et al., 2019;2021a), Haynesville (Saputra et al., 2021b), Permian (Saputra et al., 2022a;2022b), and Marcellus Shale (Saputra et al., 2022c). It is a combination of the physical scaling method and generalized extreme value (GEV) statistics.
Physical Scaling of Shale Production
Figure 1. Schematic of interior flow and exterior flow toward the stimulated reservoir volume (SRV); ℳ is the fluid mass inside the SRV.
The physical scaling method was first derived to predict gas production in Barnett Shale (Patzek et al., 2013) and black oil production in Eagle Ford Shale (Patzek et al., 2017). This method is based on a simple model of pressure diffusion toward hydraulically fractured shale geometry shown in Figure 1. Equation 1
is the approximate solution of interior flow toward the SRV. For black oil, the constants 𝜆 and 𝛽 are fixed at 1.3 and 0.55 respectively, while 𝐶 is calculated as "!
#"#$𝑃$− 𝑃%' (see Saputra et al., 2021a;2021b). For condensate or dry gas, parameters 𝐶 and 𝑎 are obtained by fitting numerical simulation results at a certain fluid composition. The dimensionless time 𝑡̃ is the ratio between elapsed time on production 𝑡 and the pressure-interfering time 𝜏.
𝑅𝐹&(𝑡̃) = 1𝐶 21 − 𝑒'()*+,-.$/05 for black oil 𝐶 (tanh(𝑎𝑡̃1 ))21( for condensate or dry gas
(1)
If we assume that there is additional exterior flow from outside the SRV, the recovery factor can be written as Equation 2 (Eftekhari et al., 2018). Where, 2𝑑 is hydraulic fracture spacing, 2𝐿 is hydraulic fracture tip- to-tip length, and 𝜅 is the initial slope between 𝑅𝐹&(𝑡̃) and square root of 𝑡̃ (see Saputra et al., 2021a;2021b).
𝑅𝐹3(𝑡̃) = 𝑅𝐹&(𝑡̃) + 𝑅𝐹4(𝑡̃), where 𝑅𝐹4(𝑡̃) = 𝜅𝑑
𝐿O𝑡̃ (2)
Equations 1 and 2 are called master curves. To predict EUR, one can scale the mass of hydrocarbon rate ṁ
to match the master curves by 𝜏 in x-axis and ℳ in y-axis, where ℳ is the initial mass of hydrocarbon in place. This scaling operation is mathematically written as an optimization problem in Equation 3.
min ℱ(ℳ, 𝜏) = T U𝑅𝐹(𝑡̃$) −ṁ V𝑡$ 𝜏 W
ℳ X
-%&' 2
$6(
(3)
In (Arias-Ortiz et al., 2022), (Haider et al., 2020), and (Saputra et al., 2018), it is shown that the physical scaling curve is as accurate as a commercial reservoir simulator with the simplicity of the decline curve analysis (DCA) method.
Generalized Extreme Value (GEV) Statistics.
The Generalized Extreme Value (GEV) distribution combines the three extreme value distributions: Type I (Gumbel, 1958), Type II (Fréchet, 1927), and Type III (Weibull, 1951), where the probability density function (PDF) of GEV is calculated using Equation 4 and plotted in Figure 2.
Figure 2. Probability density function of three Generalized Extreme Value (GEV) statistics types (I: Gumbel, II: Fréchet, III: Weibull).
The GEV distribution is robust to model various natural phenomena due to its flexibility of the shape parameter 𝜉, in addition to the location parameter 𝜇 and the scale parameter 𝜎.
𝑓(𝑥) =1
𝜎𝑡(𝑥)78(𝑒)-(:), where 𝑡(𝑥) = _`1 + 𝜉 V 𝑥 − 𝜇
𝜎 Wa
)(/7
if 𝜉 ≠ 0 𝑒)(:)=)/> if 𝜉 = 0
(4)
𝐹(𝑥) = 𝑒)-(:) (5)
The cumulative distribution function (CDF) and mean of the GEV distribution can be calculated using Equation 5 and 6 respectively.
𝑥̅ = 1
𝜇 + 𝜎(𝑔(− 1)/𝜉 if 𝜉 ≠ 0, 𝜉 < 1 𝜇 + 𝜎𝛾 if 𝜉 = 0
∞ if 𝜉 ≥ 1
, where 𝑔?= Γ(1 − 𝑘𝜉), and γ is Euler@s constant (6)
Materials and Methods
We mined the publicly available data of Eagle Ford Shale play from the EIA, USGS, The Railroad Commission of Texas, DrillingInfo, and FracFocus. Briefly, our method works as follows:
1. First, we divide the play into several spatiotemporal well cohorts based on shale geology, reservoir fluid properties, and completion date intervals.
2. For each well cohort, we convert the liquid rate 𝑞A (bbl/yr) and gas rate 𝑞B(Mscf/yr) into mass rate (ktons/yr) using Equation 7, 8, and 9. The conversion factors 𝜔A and 𝜔B are obtained as function of the GEV means of oil API gravity and gas specific gravity (SG). We use Equation 6 to calculate mean of GEV distribution.
ṁC= 𝜔A𝑞A,$+ 𝜔B𝑞B,$ (7)
𝜔A = 10)E× 5.615 × (0.3048)2𝜌A, where 𝜌A = 141.5
(131.5 + API)𝜌FGHIJ (8) 𝜔B= 10)K× (0.3048)2𝜌B , where 𝜌B= SG 𝜌GCJ (9)
3. Next, we build well prototypes for each cohort using GEV mean of annual mass rate. Using the physical scaling method (Equation 1 or 2), we extrapolate the historical well prototypes for up to two more decades.
4. To obtain oil, condensate, or natural gas forecasts, we convert back the projected well prototypes from mass rate to volumetric fluid rate, based on GEV means of gas to oil ratio (GOR) (scf/STB) and condensate to gas ratio (CGR) (bbl/MMscf), see Equation 10.
𝑞A,$ = ṁC V𝜔A+ GOR
1000 𝜔BW and 𝑞B,$ = ṁC V𝜔B+ CGR
1000 𝜔AW , where CGR = 10E
GOR (10)
5. To obtain more realistic forecasts with infill drilling conditions, we calculate probability of success, probability of well survival, and infill potential for each region of Eagle Ford Shale play.
Results
Design of Well Cohorts. To define the spatiotemporal well cohorts, we first plotted the GOR and API of all 25,707 existing wells in Eagle Ford Shale play, see Figures 3 and 4. We adopted geological boundaries from USGS (Whidden et al., 2018), to delineate two major formation extents: Eagle Ford Marl and Cenomanian-Turonian Mudstone. By tracing the spatial continuity of GOR and API values across the play,
we further divided the two geological boundaries into 8 regions. Using GEV statistics, we calculated the mean values of GOR, API, SG, and depth for each region, with the corresponding results summarized in Table 1. From mean GOR and mean API, we classified Regions 1, 2, 6, and 7 as black oil, Regions 3 and 8 as rich condensate, Region 4 as lean condensate, and Region 5 as dry gas.
Figure 3. Maps of gas-to-oil ratio (GOR) for all 25,707 existing wells in Eagle Ford Shale play.
Figure 4. Maps of API Gravity for all 25,707 existing wells in Eagle Ford Shale play.
The mean depth was further converted to the mean temperature and pressure using the temperature gradient of 0.02οF/ft (Gong et al., 2013) and the pressure gradient of 0.82 psi/ft (Nejad et al., 2015). Likewise, the mean GOR value for each region was used to interpolate fluid composition from Table 1 in (Kuske et al., 2019) and Table 1b in (Carlsen et al., 2019). To capture the temporal variations of well productivity in Eagle Ford, we further divided the 8 regions into 3 completion date intervals: [2009-2014], [2015, 2018], [2019-2023]. Finally, we obtained a total of 24 spatiotemporal well cohorts in Eagle Ford Shale play.
Table 1. Summary of reservoir and fluid properties for each Eagle Ford region obtained using GEV statistics.
Region Fluid Type GOR
(scf/STB) API
Gravity SG TVD
(ft) Temp.
(oF) Pressure (psi)
1 Black Oil 760 34.7 0.78 7,860 217 6,460
2 Black Oil 1,338 42.8 0.76 9,756 255 8,014
3 Rich Condensate 3,504 50.7 0.78 10,568 271 8,680 4 Lean Condensate 14,306 61.1 0.75 10,950 279 8,994
5 Dry Gas ∞ - 0.64 11,530 291 9,469
6 Black Oil 361 35.6 0.81 7,574 211 6,226
7 Black Oil 939 41.6 0.80 8,735 235 7,177
8 Rich Condensate 4,705 48.9 0.75 10,454 269 8,587
Table 2. Corresponding fluid composition in percentage for each Eagle Ford region.
Region 1 2 3 4 5 6 7 8
C1 36.99 41.61 54.30 69.96 91.70 33.83 38.42 58.35 C2 8.13 10.02 13.67 12.57 4.60 4.80 8.72 13.47 C3 5.86 6.70 7.84 5.18 1.80 6.14 6.12 7.19 i-C4 1.01 1.25 1.75 1.47 0.30 0.86 1.08 1.78 n-C4 3.41 3.38 3.16 2.00 0.50 3.32 3.40 2.95 i-C5 1.50 1.37 1.14 1.01 0.10 1.35 1.46 1.17 n-C5 2.19 1.87 1.14 0.73 0.10 2.09 2.09 1.06 C6 2.89 2.47 1.52 0.62 0.10 2.80 2.76 1.39 C7+ 37.22 30.54 14.71 5.67 0.00 44.02 35.15 11.87 N2 0.15 0.15 0.15 0.15 0.20 0.15 0.15 0.15 CO2 0.64 0.64 0.63 0.62 0.60 0.64 0.64 0.63 H2S 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Figure 5. Example of Generalized Extreme Value (GEV) fits of the first-year production from all 2,232 existing wells in Region 1 of Eagle Ford Shale Play, completed between 2009 and 2014. The fits are displayed as (a) Probability density function, (b) 𝜇 and 𝜎 uncertainty contour, and (c) Cumulative distribution function with confidence interval. GEV fitting parameters obtained are 𝜉 = −0.024, 𝜇 = 6.83, and 𝜎 = 4.28.
Figure 6. Historical well prototypes (mean, 𝑃!", and 𝑃#") for all 24 spatiotemporal well cohorts in Eagle Ford obtained from GEV fits of annual production. Forecast-1 and Forecast-2 are physical scaling projections of the historical well prototypes with and without considering exterior flow, respectively.
Well Prototypes. Next, we used GEV statistics to construct a well prototype for each region of the Eagle Ford Shale play. Figure 5 is an example of fitting the first-year mass rates from 2,232 exiting wells in Region 1, completed between 2009 and 2014. Figure 5a shows the superiority of the GEV PDF function as opposed to the widely used Lognormal distribution. GEV CDF also remarkably fits the empirical CDF with a narrow confidence interval. We repeated the process of fitting mass rate for each year-𝑖 of production (𝑖 = 1,2, … , 𝑡YGZ [yr]) for each of the 24 spatiotemporal well cohorts. Well prototypes are plotted in Figure 6 as the cumulative of mean, 𝑃([, and 𝑃\[ from GEV statistics.
Table 3. Additional reservoir and fluid properties in Eagle Ford Shale play.
Rock porosity, 𝝓 0.086
Rock permeability, 𝒌 0.1 microdarcy Initial oil saturation, 𝑺𝒐𝒊 0.67 Irreducible water saturation, 𝑺𝒘𝒄 0.33 Oil compressibility, 𝒄𝒐 15×10-6 psi-1 Water compressibility, 𝒄𝒘 3×10-6 psi-1 Rock compressibility, 𝒄𝝓 1×10-6 psi-1 Fracture pressure, 𝑷𝒇 1500 psi
These historical well prototypes are extended for the next two decades using a physical scaling method.
Using fluid and reservoir properties in Table 1, 2, and 3, we constructed 8 unique master curves of interior recovery factor as shown in Figure 7a for black oil and 7b for condensate or dry gas. The corresponding parameters 𝐶 and 𝑎 in Equation 1 are listed in Table 4. Notice that 𝐶 values indicate the maximum values of each recovery factor curves. We observe that the recovery factor of Eagle Ford black oil is about 8.5 – 11.7%, 63 – 77% for condensate, and 85% for dry gas.
Table 4. Parameters 𝐶 and 𝑎 used to construct master curve for each Eagle Ford region.
Region 1 2 3 4 5 6 7 8
𝐶 0.089 0.117 0.630 0.727 0.770 0.085 0.102 0.661
𝑎 - - 0.577 0.609 0.623 - - 0.587
Figure 7. Physical scaling master curves for black oil regions (a) and condensate or dry gas regions (b) in Eagle Ford Shale Play.
To construct master curves with exterior flow, we assume the tip-to-tip length, 2𝐿, of 1,200 ft and the fracture spacing, 2𝑑, of 300 ft for wells completed in [2009-2014], 190 ft in [2015-2018], and 60 ft in [2018-2023]. Finally, physical scaling projections with and without considering exterior flow are plotted in
Figure 6 as Forecast-1 and Forecast-2, respectively. The corresponding values of pressure-interfering time, 𝜏, and mass of initial hydrocarbon in place, ℳ, are listed in Table 5 for all 24 spatio-temporal well cohorts.
Table 5. Corresponding physical scaling parameters for Forecast-1 and Forecast-2 in Figure 6. Columns I, II, and III represent three completion date intervals: [2009-2014], [2015-2018], and [2019-2023] respectively.
Region
Forecast-1 (without exterior flow) Forecast-2 (with exterior flow) 𝝉 (years) 𝓜 (ktons) 𝝉 (years) 𝓜 (ktons)
I II III I II III I II III I II III
1 10.0 8.5 9.1 283 368 462 8.0 7.6 9.0 205 302 437 2 8.6 5.9 4.8 287 331 328 6.4 5.0 4.6 204 265 307 3 3.7 1.8 1.3 83 79 90 2.6 1.3 1.2 57 60 84 4 3.2 2.3 1.8 86 91 111 2.2 1.9 1.7 59 71 103 5 4.1 4.8 4.1 91 167 190 2.9 4.2 4.1 63 134 180 6 17.8 17.7 9.1 363 494 527 17.8 17.0 9.0 288 418 499 7 9.0 8.0 6.8 250 296 296 7.5 7.1 6.7 184 242 279 8 1.8 1.8 2.1 30 44 35 1.4 1.3 2.0 21 34 33
Probability of Well Survival. The extended well prototypes plotted in Figure 6 are ideal cases assuming that there are no external factors affecting field production such as permeability reduction, condensate banking, or field pressure depletion. To account for these factors, we calculate a purely data-driven probability of well survival as the ratio between the number of active and total wells for each year of production. Figure 8 shows an example of the probability of well survival for Region 4 of Eagle Ford Shale play. If we project the trend of probability values using a parabolic curve, we obtain an approximate maximum time of survival, 𝑡_`Ja, for each region as its interception with the x-axis. In this case, we obtain that 𝑡_`Ja is 13.2 years for Region 4. Table 6 lists all 𝑡_`Ja values for all Eagle Ford regions. We then used these 𝑡_`Ja values to stop the well prototypes from producing forever, which may overestimate the available resources.
Figure 8. Example of probability of well survival plot for Region 4 of Eagle Ford Shale play. The maximum time of survival is obtained as the intercept of the parabolic curve with the x-axis.
Base Forecasts. The corrected well prototypes using survival probability can be used to history match the production of existing wells after conversion to volumetric rate. Results are shown in Figures 9, 10, and 11 for black oil, condensate or natural gas liquid (NGL), and natural gas, respectively. We selected 11,400 existing wells in Regions 1, 2, 6, and 7 to construct a base forecast for oil production, 12,880 existing wells in Regions 3,4, and 8 for NGL production, and all 25,707 wells in Regions 1-8 for gas production. In all
cases, we observed that the 24 physics-based and data-driven well prototypes are robust and precisely match the historical production rate and cumulative. For the base or “do-nothing” scenario, all existing wells in Eagle Ford will ultimately yield 2.53 Gbbl of oil, 2.79 Gbbl of NGL, and 25.67 Tscf of natural gas by 2035.
Figure 9. Crude oil rate (a) and its cumulative (b) for 11,400 existing black oil wells in Eagle Ford Shale play. Forecast-1 and Forecast-2 are physical scaling projections with and without considering exterior flow, respectively.
Figure 10. Natural gas liquid (NGL) rate (a) and its cumulative (b) for 12,880 existing condensate wells in Eagle Ford Shale play. Forecast-1 and Forecast-2 are physical scaling projections with and without considering exterior flow, respectively.
Figure 11. Natural gas rate (a) and its cumulative (b) for all 25,707 existing wells in Eagle Ford Shale play. Forecast-1 and Forecast-2 are physical scaling projections with and without considering exterior flow, respectively.
Probability of success. To calculate infill potential, we started by calculating the probability of success based on field data. Figure 12 illustrates the probability of success calculated in Region 1 of Eagle Ford Shale play. We first covered Region 1 with grid cells with the area of 1 square mile, and marked the cells containing non-productive wells (plug and abandoned, shut-in, or zero production) as dry cells (black), and the productive ones as productive cells (orange). The probability of success is simply calculated as the ratio of the productive cells to total marked cells. In this case, the probability of success for Region 1 is 0.74. We repeated this process for all Eagle Ford regions, and summarize the corresponding results in Table 6.
Figure 12. Example of calculating the probability of success in Region 1 of Eagle Ford Shale play. Each grid represents a 1-square mile well path.
Infill Potential. Figure 13 illustrates the calculation of infill potential for Region 1 of Eagle Ford Shale play. We first plotted the lateral length sections of all 2,332 existing wells in Region 1, and observed that the majority of wells in this region were completed with a predominant northwest-southeast (NW-SE) direction. This is an indication that the minimum horizontal stress, 𝜎YCb, in the formation is also in the NW- SE direction, as operators tend to drill wells parallel to 𝜎YCb to optimize hydraulic fractures. Using 1 square mile grid cells, we counted the number of wells intersecting each cell as the well density (wells/mi2). Next, the number of potential wells for infill drilling was calculated as the difference between infill spacing (listed in Table 6) and well density. After setting all negative values to zero, we summed up the number of potential wells and multiplied them by the corresponding probability of success. Results are shown in Table 6. We concurred that there are about 50,115 potential wells that can be drilled across the Eagle Ford Shale play.
Table 6. Summary of maximum time of survival, probability of success, and parameters used to calculate infill potential for each region in Eagle Ford Shale play.
Region Maximum time
of survival (years) Probability
of success Total
area (mi2) Number of
existing wells Infill
spacing (wells/mi2) Infill Potential (wells)
1 15.87 0.74 4,186 2,235 5 12,336
2 14.95 0.92 2,474 8,390 7 6,352
3 15.42 0.88 1,844 8,507 8 3,842
4 13.18 0.96 1,373 4,302 9 5,224
5 11.46 0.44 3,704 1,385 4 5,484
6 14.86 0.88 2,088 403 5 8,429
7 13.01 0.83 1,144 372 6 4,969
8 13.13 0.64 1,397 71 4 3,480
Drilling Schedule. We construct a future drilling program based on the value of infill potentials in each Eagle Ford Region. Figure 14a shows the historical rig count and drilling rate for Eagle Ford from EIA and DrillingInfo. By the end of 2022, there were about 120 wells drilled per month, and we used this drilling
rate to schedule future drilling rates in Figure 14b. Each number (1-8) corresponds to the time when there are no more wells left to drill in each Eagle Ford Region.
Figure 13. (a) Lateral section plots of 2,332 existing wells in Region 1 of Eagle Ford Shale play, (b) map of well density, and (c) map of infill potential
Figure 14. (a) Historical rig count and the number of wells completed in Eagle Ford Shale play. (b) Infill drilling schedule for Eagle Ford Marl and Cenomanian-Turonian Mudstone regions.
Figure 15.The forecasted oil rate (a) cumulative oil (b) based on the drilling condition in Figure 14.
Figure 16. The forecasted NGL rate (a) cumulative NGL, and (b) based on the drilling condition shown in Figure 14.
Figure 17. The forecasted gas rate (a) cumulative gas, and (b) based on the drilling condition shown in Figure 14.
Infill Forecasts. Using the drilling schedule in Figure 14b, we constructed infill forecasts for oil (Figure 15), NGL (Figure 16), and natural gas (Figure 17) in Eagle Ford Shale play. This is the ultimate step to determine the field-wide estimate ultimate recovery (EUR) of Eagle Ford. By drilling additional 33,238 horizontal wells in Eagle Ford Marl (Regions 1-5), the EUR increases to 7.48 Gbbl of oil, 4.86 Gbbl of NGL, and 85.85 Tscf of natural gas. On the other hand, by drilling additional 16,878 wells in Cenomanian- Turonian Mudstone (Regions 6-8), the EUR further increases to 11.13 Gbbl of oil, 5.21 Gbbl of NGL, and 89.37 Tscf of natural gas.
We observed a strong trend of declining NGL rate as most productive condensate regions in Eagle Ford Marl (Regions 3 and 4) are already over-drilled. Meanwhile, condensate production from Cenomanian- Turonian Mudstone (Region 8) is not as productive as Regions 3 and 4. On the other hand, the black oil regions of Cenomanian-Turonian Mudstone (Regions 6-7) are productive and will become future hotspots for the Eagle Ford crude oil production. Natural gas production comes from all Eagle Ford regions, even from black oil regions. However, Cenomanian-Turonian Mudstone regions will not contribute a high amount of natural gas as they lack proven dry gas-containing formations.
Discussion
We have verified the robustness of our hybrid physics-based data-driven method to history-match the production from all 25,707 existing wells in Eagle Ford Shale play. Unlike (Whidden et al., 2018), (EIA, 2023), and (Gong et al., 2013), we further refined the spatial production regions into three temporal cohorts based on completion date intervals. Similar to other shale plays in the U.S., Eagle Ford has undergone the advancement of completion technologies over the last decade that makes newly completed wells typically more productive than the older ones. Figure 18 shows that over ten years, lateral length, fracturing water intensity, and proppant intensity have doubled.
The massive increase in fracturing water and proppant intensity will likely increase the volume of fracturing stages which leads to more access to the mass of hydrocarbon available within the stimulated reservoir volume (SRV). The increase in lateral length should raise the amount of hydrocarbon produced for the newly completed wells. Therefore, it is necessary to model newly completed wells separately from older ones. However, it is not always the case that production increases linearly with an increase in lateral length and fracturing water and proppant intensity. The extended well prototypes in Figure 6 show that the doubling of total production only occurs specifically in Region 5. Most regions show approximately a 1.5 increase in production, while some regions show little to no production increase.
Figure 18. Time-lapse of lateral length (a), fracturing water intensity (b), and proppant intensity (c) in Eagle Ford Shale play.
Lastly, we compared our estimated ultimate recovery (EUR) and remaining resources estimate with other publicly available results by USGS (Whidden et al., 2018), (EIA, 2023), and (Gong et al., 2013). We define remaining resources as the difference between the total EUR with the EUR from existing wells. Table 7 summarizes the values of EUR and remaining resources for oil, natural gas, and NGL from this study and the other three published results. Our EUR estimate for oil of 11.13 Gbbl is close to (Gong et al., 2013), while our EUR estimate for gas of 89.37 Tscf is almost identical to EIA. The remaining resources of oil, gas, and NGL are close to the USGS results.
Table 7. Comparison of estimated ultimate recovery (EUR) and remaining resources of Eagle Ford Shale Play between this study and other publicly available estimates
Estimated Ultimate Recovery (EUR) Remaining Resources Oil (Gbbl) Gas (Tscf) NGL (Gbbl) Oil (Gbbl) Gas (Tscf) NGL (Gbbl)
This study 11.13 89.37 5.21 8.60 63.70 2.42
EIA (2023) 15.50 89.60 22.90 - - -
USGS (2018) - - - 8.52 65.98 1.89
(Gong et al., 2013) 11.74 122.00 - - - -
Conclusions
We developed a robust, transparent, hybrid physics-based data-driven method for assessing play-wide ultimate recovery of Eagle Ford shale play. Our new reserve-assessment method considers not only the shale play geology, but also the production dynamics of hydraulically fractured wells, the variability of Eagle Ford reservoir fluids, and well attrition.
Results indicate that the total 25,707 existing wells in the Eagle Ford will ultimately yield 2.53 Gbbl of crude oil, 2.79 Gbbl of natural gas liquid (NGL), and 25.67 Tscf of natural gas by 2035. There are 50,115 potential wells that can be drilled across 18,665 sq. miles of the Eagle Ford play, and with future drilling programs, there will be additional 8.60 Gbbl of crude oil, 2.42 Gbbl of NGL, and 63.7 Tscf of natural gas by 2065.
The Cenomanian-Turonian Mudstone regions will be hotspots for future oil production in the Eagle Ford Shale. However, more exploratory wells should be drilled to test the productivity of condensate and dry gas in this formation.
We believe that the new hybrid, data-driven and physics-based approach developed in this paper is the future of production forecasting and reserves estimation in all shale plays which also provides an objective way of avoiding estimates that are unrealistically low or high.
Acknowledgments
The authors thank the University of Texas at Austin’s Research Consortium on Formation Evaluation (jointly sponsored by AkerBP, Baker Hughes, BP, Chevron, CNOOC International, ConocoPhillips, ENI, Equinor, ExxonMobil, Fieldwood Energy E&P Mexico, Halliburton, Inpex Corporation, Oxy, Petrobras, Repsol, Schlumberger, Total Energies, Wintershall, and Woodside) and the Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC) at King Abdullah University of Science and Technology for supporting this research.
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