Vaca Muerta 18 Meses

URTeC: 1965548

Production Analysis and Forecasting of Vaca Muerta Shale Wells in Argentina: Case History-Based Herrero F., Maschio L., Maria S., Pluspetrol

Copyright 2014, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2014-1965548

This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA, 25-27 August 2014.

The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper without the written consent of URTeC is prohibited.

Abstract

Vaca Muerta is an organic shale and one of the main source rocks for conventional reservoirs in the Neuquen Basin in Argentina. According to the 2013 United States Energy Information Administration (EIA) report, Vaca Muerta could produce 16 billion barrels of liquids and 308 TCF of gas (EIA, 2013). Up to the time of writing, only about 200 wells have been drilled to test Vaca Muerta, over 90% of them vertical.

Multiple wells drilled by Pluspetrol in different Neuquen basin locations were selected for this paper to explore and test Vaca Muerta productivity. A wide range of data was gathered. Some examples are: a full set of logs, wet samples, petrophysic and geomechanic tests in cores, geochemistry in cut samples and PVT fluid samples. Most of the wells were completed with two fracture stages while some others had only one stage in order to test the most prolific horizons individually.

During the production testing, a careful and detailed oriented surveillance program was designed to gather high quality data. Between 14 and 20 months of daily rates and pressure information is available. Additionally, several pump in/flow back tests and extended build ups (more than 40 days) were performed on these wells. Some of these wells flow naturally while others had an artificial lift installed providing information on different production conditions.

This information was combined to make a full reservoir characterization. A full rate transient analysis workflow was carried out in six wells. This includes straight line plots, type-curve analysis, analytical model history matching and probabilistic forecasting. In addition, pressure dependent permeability and average reservoir pressure increase due to fracture injection fluids effects on well performance will be discuss in this paper. Finally, a set of conclusion with the findings are presented. The aim of this paper is to summarize the analysis and findings to characterize Vaca Muerta as an unconventional reservoir.

Introduction

The importance of unconventional reservoirs to successfully supply the current and constantly increasing need of energy of the world is well recognized. Multiple energy outlooks from the major E&P companies place the unconventional reservoir as one the key sources to supply the world energy demand (Exxon Mobile Outlook of energy, 2014). The EIA estimates a remarkable 7,299 TCF of gas and 345 billion barrels of oil available as unconventional world technically recoverable resources (EIA, 2013).

Argentina has six productive basins (Legarreta and Villar, 2012) currently producing about 4.3 BFC/d of gas and 571,278 bbl/d of oil (IAPG data base, 2013). The Neuquen Basin is one of the most important, producing about 53% of the total gas and 40% of total oil production within the country. According to the EIA, Argentina has world-class shale gas and shale oil potential, possibly the most prospective outside North America, primarily within the Neuquen Basin (EIA, 2013). One of the most prolific, extensive and with the greatest quality source rock in the

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Min Average Max Unit

Top VM 9,807 9,971 10,135 ft

Thickness 328 361 410 ft

Porosity 5% 6.5% 8% -

Matrix Perm 60 134 218 nd

TOC 0.8% 3.5% 10% -

Kerogen type -

Ro 0.78 0.85 0.94 %

Vstatic 0.23 0.26 0.28 Mpsi

Gstatic 0.66 1.6 8.45 MPsi

Estatic 1.25 4 3.29 Mpsi

Poral Pressure gradient 0.74 0.78 0.82psi/ft

API 23 24 26 API

GOR 200 225 250 scf/stb

II

Neuquen Basin is Vaca Muerta. It is estimated that Vaca Muerta has 308 TCF of risked technically recoverable gas and more than 16 billion barrels of risked technically recoverable oil (EIA 2013). Over the last five years multiple companies have been exploring Vaca Muerta. Today, there are about 200 wells on production targeting Vaca Muerta, however only a few of them are horizontal.

Pluspetrol has been one of the first companies that initiated the exploration for unconventional resources within Argentina. First exploring tight gas reservoirs and then shale oil and shale gas. Six of the wells drilled in this exploration campaign were selected for this paper.

Vaca Muerta Description

Vaca Muerta is a world class source rock and shale reservoir in the Neuquen Basin in Argentina. This shale formation was deposited during the Tithonian, late jurassic transgression that took place in the Neuquen Basin (Fernandez Badessich and Berrios, 2012). It covers most of the basin with 7,400,000 acres with thickness ranging from 100ft to over 1,500ft. Vaca Muerta generation efficiency as source rock is well documented (Cruz et al, 1996; Cruz et al, 1996) having kerogen type II oil and gas prone (Villar et al, 2006). The TOC ranges from 2% to 12% in the base. The shale maturity measured by the vitrinite reflectance ranges from Ro less than 0.5% to Ro over 3%. Thus, this shale play contains all the fluid windows raging from black oil to dry gas.

The project

Pluspetrol started an ambitious project to explore Vaca Muerta in the concessions where the company operates. Multiple vertical wells were drilled to test Vaca Muerta in different locations within the basin. This information was used to build a geological and geomechanical model aiming to find a sweet spot to drill the first horizontal production pilot. To accomplish this objective, data was gathered from every single domain. A full set of specialized logs were run in each well. From a reservoir and production stand point, a comprehensive surveillance program was designed to obtain high quality production and pressure data. This information is key to characterize this kind of reservoirs that stay long periods in transient flow. Lab petrophysic and geomechanic characterization was performed on three cores. Two PVT samples were analyzed showing a crude oil of 26 API for Block A and 23 API for Block B. Different pumping-in decline pressure analysis were executed in each well to calibrate geomechanical and reservoir models. All of the wells were stimulated with one or two hybrid hydraulic fractures. Between 250,000 lb and 800,000 lb per stage was usually pumped depending on pay interval. Microseismic monitoring was performed in three of the six wells. Multiple production loggings (PLT) were ran in each well to characterize each part of the production life, ranging from early PLTs during stimulation fluid flow back, to PLTs in the later part of the production history. Table 1 summarizes typical parameters obtained from well measurements in Vaca Muerta.

In this paper all the available information including more than two years of production data from Pluspetrols shale oil vertical wells is analyzed to determine the main reservoir parameters and estimated ultimate recovery.

Methodology

It is well documented that analyzing shale oil wells performance has proven to be challenging (Clarskson et al. 2010). There are multiple variables and effects that have to be evaluated together in order to fully understand a shale as reservoir. Some challenges associated with these reservoirs are: long transient periods due to extremely low permeability, complex reservoir behavior, dual porosity effects, multi-layer behavior, multi-phase flow, stress-sensitive permeability, production from multiple intervals and massive hydraulic fracture stimulations performed.

Table 1: typical reservoir parameters

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Classic material balance methods do not apply for unconventional reservoirs due to the long periods of well closures that would be necessary to estimate reservoir pressure (Lee et al. 2010). There are three methods most widely used to analyze well performance from unconventional wells and calculate Estimate Ultimate Recovery (EUR):

I. Decline curve analysis (DCA) II. Rate transient analysis (RTA)

III. Complex numerical simulation

I. Decline curve analysis (DCA) These models were originally thought to handle wells that were producing under a boundary dominated flow regime. Shale reservoirs usually stay in transient flow for long times (Lee and Sidle, 2010). Thus, experience with the application of DCA to shale gas/oil reservoirs has shown that misleading conclusions may be extracted from traditional models, such as Arps. Several tailored models were developed to perform decline curve analysis to unconventional wells. Some examples of these adapted models are: Stretched Exponential Model (Valko and Lee, 2010), Power Law Model (Ilk et al, 2008) and Duong Model (Duong, 2011).

DCA is a relatively accurate method for EUR calculation if enough production data is available. Due to its simplicity, it is an excellent method when a quick production forecast estimate is required and accuracy is not the main concern. However, due to its empirical nature, these models do not add in gaining insights about the reservoir parameters estimation. Furthermore, EUR forecasting may not valid if well operating conditions change in the future, for example assessing the EUR increase of installing an electrical submergible pump.

II. Rate Transient Analysis (RTA)As stated by Clarkson, RTA involves the interpretation of characteristic flow regimes, which evolve during production of a well, to extract quantitative information about hydraulic fracture and reservoir properties. The procedure and theory for RTA is analogous to pressure transient analysis (PTA); in fact, modern concept of RTA is to analyze production data like one would a long term drawdown test, which is a classic well test procedure (Clarkson 2011). Reservoir parameters can be extracted from RTA. Furthermore, effects such as pressure dependent permeability and dual porosity behavior can be accounted for in the analysis. Moreover, these methods can history match and forecast wells with changing operating conditions, such as installing artificial lift in the life of the well.

III. Complex Numerical ModelsComplex numerical reservoir and fracture simulations in shale reservoirs have been documented by Cipolla (Cipolla et al., 2009; Cipolla et al., 2011). These models are based on a discrete gridding of the entire reservoir, including the network fractures, matrix blocks and unstimulated areas. The amount of information and data needed to populate these models is usually massive and often unknown. Furthermore, even in the hypothetical case that all the information is available, building and history matching with these models is extremely time consuming. Nevertheless, if all the information and computing capabilities needed to build these complex models are available, then they are an excellent and reliable tool to understand and predict reservoir behavior.

From the three methods listed above, RTA is selected for this paper. The next section describes the workflow used in all six wells analyzed and the two shown in this paper. The workflow is a modified version of the one proposed by Clarkson (Clarkson et al., 2011).

Workflow description

The workflow applied in this study can be described in the following five steps: data validation, flow regime identification with type curves, parameter extraction from straight line plots, analytical model calibration and probabilistic forecasting.

1) Data validation (QA/QC):

A detailed and careful quality control and quality assurance on gauge data and reported volumes is needed before any analysis is performed. Additionally, production data was interpolated and smoothed out using a locally weighted

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scatter plot smoothing (LOESS) algorithm (Cleveland et al., 1979). This procedure simplifies the identification of the flow regimes.

2) Flow regime identification with type curves:

Type curve matching involves fitting production history data with theoretical and/or empirical solutions to flow equations that are cast in dimensionless variable format. Fetkovich (Fetkovich et al., 1980) was the first to extend the concept of using type curves, previously only used in well testing analysis, to the analyzed production data. Several modern type curves were later developed (Blasingame et al. 1991; Agarwal-Gardner et al., 1998). These type curves are similar to Fektovich type curves. However, they also incorporate the flowing pressure data along with production rates and they use analytical solutions to calculate hydrocarbons-in-place. In addition to flow regime identification, reservoir parameters such as fracture half length, permeability and Original Oil In Place (OOIP) can be extracted from type curve analysis.

In this paper, Blasingame type curve will be used for flow regime identification. Figure 1 shows Blasingame type curve matching to two synthetic cases. Figure 1a shows a well that begins producing under linear flow and later changes to boundary dominated flow. A slope of -1/2 is characteristic of a linear flow, whereas a slope of -1 is characteristic of boundary dominated flow. Figure 1b shows a pseudoradial period between the linear (beginning) and boundary dominated flow (end). This pseudoradial flow can be observed as an upward deviation from the -1/2 slope. Figures 1c and 1d shows a schematic of the models used to build these synthetic cases. In the first well, reservoir with (xe) equals total fracture length (2xf) and therefore only linear flow is possible during the transient period. In the second well, xe is bigger than 2xf and therefore there exist flow from outside the tip of the fracture allowing for a period of pseudoradial flow. These same models will be used for the analysis presented in this paper assuming a bounded drainage area with an effective permeability and a principal planar fracture.

-1/2 slope

-1 slope

-1/2 slope

-1 slope

Figure 1a and 1b show two synthetic cases analyzed with Blasingame type curve. Case 1a represents a well where flow regime goes from linear to boundary dominated. Case 1b represents a well where there is a pseudoradial period between linear and boundary dominated flow. Figures 1c and 1d show the schematic view of the wells used to generate cases 1a and 1b respectively. In figure 1c the fracture length is equal to the reservoir extension, whereas in figure 1d fracture length is smaller than reservoir extension.

Pseudoradial

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3) Parameter extraction from straight line plots:

Pressure response in a fractured well can be described by the diffusivity equation for linear flow (Bourdet et al, 2001):

= 4.064 + This equation can be simplified by the following expression:

!"# = $ + % The linear flow chart, plots normalized pressure [(Pi Pwf)/q] versus square root time. Linear flow data should appear as a straight line on this plot and m and b can be extracted from the slope and y-intercept of this straight line. A useful parameter for linear flow characterization is the Linear Flow Parameter (LFP) defined by the following expression (modified from Anderson et al., 2010):

'() = 4 According to the previous equations, LFP is related with m by the following expression:

'() = *$ +Therefore, knowing the values of viscosity, porosity, fluid volumetric factor and total compressibility, LFP can be estimated from the linear flow chart. Another important parameter to characterize linear flow is the apparent skin (s) which accounts for all the pressure losses inside the fracture, such as damage in the fracture face, finite conductivity behavior, etc. This parameter can be related with b by the following equation:

, = -%Figure 2 shows the linear flow chart for two synthetic cases. Both show a first period of linear flow followed by a period of boundary dominated flow, which can be identified as an upward deviation from the straight line. The plot on the left shows how a well with infinite conductivity and no pressure loss inside the fracture looks like on this type of chart. The plot on the right illustrates a y-intercept value different than cero, which is characteristic of wells with significant pressure drop inside the fracture. A more detailed description of wells with apparent skin can be found in the literature (Nobakht and Mattar 2012).

Figure 2: Two synthetic cases analyzed with the linear flow chart. The plot on the left shows a cero Y-intercept which corresponds to a well with no pressure loss inside the fracture. The plot on the right shows a Y-intercept which corresponds to a well with a considerably pressure loss inside the fracture.

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The other straight line plot that will be used in this step is the Flowing Material Balance (FMB). This method is similar to a conventional material balance analysis; however, it requires no shut-in pressure data, except initial reservoir pressure. Instead, it uses pressure normalized rate and material balance time to create a simple linear plot. When the well has reached a boundary dominated flow, this trend can be extrapolated to x-intercept to obtain OOIP. A full derivation of this method can be found in the literature (Mattar and Anderson, 2003).

A drawback of this method is that most of shale wells may not exhibit boundary dominated flow for many years. Therefore, it may take too long to estimate OOIP. If the well is still under transient flow, the extrapolation of the last trend would yield a minimum value of fluids in place. Figure 3 shows an example of a synthetic case. A linear trend can be seen in the last period of the life of the well. This trend corresponds to a boundary dominated flow regime. It can be seen that if the extrapolation is made too early when the well is still under transient flow, the estimation of OOIP will be pessimistic.

4) Analytical model calibration:

Many commercial analytical and numerical simulation tools are available to history match pressure and rates in shale oil wells by calibration of model inputs. An analytical model was chosen in this work because of its speed and easiness to perform a history match and sensitivities compared to numerical models. Considering that reservoir pressure was never below bubble point and water rates become negligible after the first weeks of production, only single phase flow occurred in most of the life of the wells. Thus, the analytical model is an accurate tool for this kind of wells.

5) Probabilistic forecasting:

As pointed out by Anderson, if a well is flowing under boundary dominated flow, RTA would provide a reliable characterization of hydrocarbon pore volume. However, when long-term transient flows are present, there is significant uncertainty associated with the OOIP, and, although the quality of the history match may be excellent, the solution may be non-unique. For this reason, a probabilistic approach was selected for production forecasting. The probabilistic approach differs from the conventional deterministic approach in which a single best fit model is the result. The probabilistic approach acknowledges that there may be multiple sets of input model parameters for which a satisfactory history match is available and provides multiple realizations for both the input and output terms using a simplified uncertainty model (Anderson and Liang, 2011).

This workflow was applied to six vertical wells producing from Vaca Muerta. The next section will show the analysis on two of these wells in detail.

Well A: Workflow Description Step by Step

Well A was hydraulically fractured with hybrid fluid in 2 stages. A stimulation of 650,000 gal of fluid and 860,000 lb of Bauxite proppant at 60 bbl/min was pumped. After the stimulation, the well was opened to flow back using a 2mm choke. Since then, it has been producing by natural flow on a 2mm choke for 16 months.

Figure 3: Synthetic case analyzed with flowing material balance. Linear extrapolation yields OOIP.

Early extrapolation

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1. Data validation (QA/QC):

Figure 4 shows production rate and estimated Bottom Hole Pressure (BHP). Rate data was normalized for confidentiality; values of 100 represent the maximum production. The first 10 days the well was flowing through casing. Then, the well was shut in to install the 2 7/8 production tubing. After day 30 the well started producing through tubing. Pressure values from day 11 until day 30 were interpolated because pressure could not be measured during the first shut in. For the rest of the life of the well, BHP was estimated from wellhead pressures using Hagedorn and Brown correlation (Hagedorn et al., 1965). This correlation was calibrated using multiple dynamic gradients measured during the production life. A downhole gauge was installed before starting the buildup period at day 300. Unfortunately, due to gauge problems only the first 13 days of the buildup period were recorded. The rest of the BHP during build up was calculated from wellhead pressure measurements.

Rates were measured using a gauge tank. Thus, LOESS algorithm was applied on oil production data to interpolate and smooth the data. Water production only lasted for the first three days and became negligible after the fourth day of production through tubing. Only about 14% of the water injected during fracture treatment was recovered. Gas rates were measured during the first 50 days of production showing an average GOR of 225 scf/stb. For the rest of the production history the GOR was assumed constant. This is a valid assumption considering that the oil bubble point (Pb) is 1650psi, well below the ~4000 psi bottomhole flowing pressure.

2. Flow regime identification:

The different flow regimes were analyzed using the Blasingame type curve (Figure 5). The first 10 days of flowback through casing data is not shown. Three different flow periods can be identified:

I. In the first ten days an upward deviation from the -1/2 slope is seen, which can be related to a supercharging effect due to fracture fluids injection. No bi-linear flow is observed in the early days of production.

II. Then, between the 10th day and the buildup, a clear linear flow with -1/2 slope is seen.III. After the buildup, the -1/2 trend is lost. However, after 2 months of production, the slope resumes to

the previous -1/2 slope.

Blasingame type curve analysis suggests that Well A is still under transient linear flow regime, and no effects of boundary dominated flow have been observed so far.

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Bottom Hole Pressure

Figure 4: 16 months of production history for well A.

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3. Parameter extraction from linear flow plots:

The linear flow chart is shown in Figure 6a. This plot suggests that this well is still in linear flow, being consistent with Blasingame type curve analysis. From the slope of this chart, the LFP is estimated to be 22,383 md0.5ft2. Unfortunately, there is no independent measure of permeability or fracture half length, to estimate one of these variables separately. However the LFP is used to compare wells rather than each individual value. The Y-intercept in Figure 6a is almost cero, suggesting a high fracture conductivity.

Material balance plot is shown on Figure 6b. Since the well is still in transient flow, there is no stabilized linear trend. Extrapolation of the last trend gives an estimated OOIP of 340 Mstb, which is considered as the minimum estimate of OOIP. Similarly, using the same principle, the minimum area of investigation drained so far is 6.2 acres.

Post build up period

-1/2 slope

Figure 5: Blasingame type curve analysis for well A. Ten months of linear flow can be seen before build up. Post build up data resumes to the original linear trend after 2 months of production

Figure 6a: Linear flow chart for well A showing linear flow with small Y-intercept.

Figure 6b: Flowing material balance for well A. Extrapolation yields a pessimistic value of OOIP because transient flow is still taking place.

First ten days of production 10 months

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4. Analytical model calibration:

Parameters extracted from the linear flow plots were used as a starting point for the analytical model calibration. Additionally, estimates of initial reservoir pressure, porosity, saturations and formation compressibility were incorporated.

It is expected that a robust reservoir model would replicate real well measurements both during production and build up period. Figures 7 to 10 show the three iterations done to build such a robust model. The first analytical model was built doing an automatic history match process to calibrate fracture half length, conductivity, effective permeability and drainage area as shown in Figure 7 and table 2. It can be observed that although this model achieves a fairly good match during the drawdown periods, it fails to model the build-up period.

A second iteration was done by trying different and more complex effects such as: dual porosity model, varying skin with time and pressure dependent permeability. Only pressure dependent permeability effect showed an improvement in the matching quality. An exponential equation (Pedroza, 1986) was used to account for pressure dependent effects which relate permeability and pressure by the following expression:

./01 = .23 4/02 01The parameter is known as reservoir compliance. The higher the value, the bigger the pressure dependence effect. However in spite of incorporating a pressure dependent permeability in the model, it was not enough to match the buildup pressure measured. The results of the second iteration, where only pressure dependent effect is used, are shown in Figure 8 and table 2.

A third iteration was done to improve the buildup matching quality. This time, other parameters that at first were assumed as known and constant, were now allowed to change. It was concluded from this analysis that the only possible way to match the whole history, including the drawdown and buildup, was by increasing the estimated initial reservoir pressure by 20%. The result of this analysis is shown in Figure 9 and table 2.

The same conclusion was drawn when the rest of the six wells were analyzed. This includes well B and well C whose BHP was effectively measured with a downhole gauge, thus a problem related to measures in this well data was discarded. Therefore, the following questions needs to be answered: Was the initial reservoir pressure estimation wrong? Or is it possible that fluid volumes injected during fracture treatment could have increased average reservoir pressure about 20%? In order to answer these questions it is important to bear in mind that the fluid volume injected in the fracture treatment is anything but small or insignificant. This volume is equivalent to the cumulative gross production volume of the first two years or about 25% of the EUR of the well. Thus, the injected volume is not negligible to the reservoir. Additionally, downhole pressure measures just before opening the well after fracture stimulation was about 20% higher than the original reservoir pressure. At first glance, this pressure increase was thought to be a local effect in the near wellbore. However, the analysis of production data may suggest that rather than a local effect, an average pressure increase of 20% is taking place.

Initial formation pore pressure was estimated using data obtained from sonic logs for shale/mudstone rich horizons from Vaca Muerta and surrounding formations. A gamma ray cut-off is used to exclude sandstone and sandy horizons in selected intervals for pore pressure prediction. With increasing depth, normal shale/mudstone compaction will result in increasing velocity. Overpressure methods are based on the under compaction of shales on overpressure horizons which are indicated by the divergence of sonic data from the virgin shale compaction trend. These deviations are then related to effective stress and, therefore, to pore pressure. These estimations are calibrated with pump in decline and pump in flow back tests when available and drilling data such as mud weight, influxes and losses as well. For the case of this well, a pump in flow back test (Nolte and Smith, 1981) was performed yielding a fracture gradient of 0.95 psi/ft. Thus, original pore pressure should be below this value and well below the decline matching value. Reservoir pressure increase in the SRV area will be further reviewed and discussed later.

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Figure 7: Analytical Model I history match for well A. Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). BU pressure is not properly matched in this model.

Figure 8: Analytical Model II history match for well A Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). BU pressure match is improved in this model, but still no successful match is achieved.

Figure 9: Analytical Model III history match for well A. Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). Successful history match of both drawdown an BU periods.

Table 2: Matching parameters for models I (left), II (centre) and III (right) Well A

Initial Pressure 8134 psi

Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%

Water Saturation 30%

Form. compressibility 7.3E-06 1/psi

Effective Permeability 0.0196 md

Fracture half length 172 ft

Area 15 acres

Perm Compliance 0 1/psi


Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%





Area 15 acre

Perm Compliance 4E-04 1/psi


Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%





Area 15 acre

Perm Compliance 3.5E-04 1/psi

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5. Probabilistic forecasting:

Probability distributions were estimated for each of the variables that involve uncertainty (table 3). Porosity and saturations distributions were estimated from log and core analysis. Net pay distribution considers uncertainty in cutoffs and net propped height (total thickness is 350 ft). Formation compressibility distribution was estimated from geomechanical core analysis. The lower bound of the stimulated reservoir volume (SRV), was estimated from the flowing material balance of this well and analogs, and the upper bound from microseismic mapping. Flowing material balance had shown a minimum drainage area of 6.2 acres. Usually this value would have been used as P90 estimated; however analog wells, well B to F, showed areas of investigation from 6.2 up to 14 acres without reaching boundary dominated flow. Thus, based on the offset wells and knowing that this value is usually pessimistic it was used as P99.

Reservoir pressure was maxed out at 9,800 psi for the Montercalo run. Any value above this number is impractical since the BHP measured after stimulation and before flowing back starts was 9800 psi. Finally, it was seen that no other pressure compliance than 3.5e-4 1/psi would achieve a history match; therefore, this parameter was fixed at this value for this simulation.

A Montecarlo simulation was run with the input variables showed in table 3. Additionally, effective permeability, fracture half-length and conductivity were allowed to vary in each Montecarlo iteration to match production rates and pressures. This means that, in each Montecarlo iteration, a new history match is performed with the analytical model. Iterations were a successful history match was not achieved were discarded according to a fitting error criteria. Figures 10 and 11 show the results suggesting a recovery factor between 2% and 6% of OOIP within the SRV.

Variable Distribution P90 P10 Unit

Initial Pressure psi

Porosity Normal 5% 8%

Net Pay Normal 98 230 ft

Oil Saturation Normal 50% 80%

Water Saturation

Form. Compressibility Normal 5.8E-06 9.0E-06 1/psi

Reservoir Area Lognormal 9.6 30 Acres

Perm Compliance 1/psi

100% - Oil Saturation

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Table 3: Input variables for probabilistic forecast Well A Figure 10: Recovery factor histogram for well A

Figure 11: Probabilistic forecast for well A

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Well B workflow steps

Well B was hydraulically fractured with hybrid fluid in 2 stages; 650,000 gal of fluid and 830,000 lb of bauxite proppant at 60 bbl/min was pumped.

1. Data validation (QA/QC):

Figure 12 shows 18 months of production rates and BHP. During drawdown, BHP were estimated using Hagedorn & Brown correlation calibrated with dynamic gradients measured regularly. Bottomhole and wellhead pressures during build up were both effectively measured with a downhole and surface gauge respectively. During the first shut in for tubing installation, wellhead pressures could not be measured; therefore pressure data was interpolated.

Rates were measured similarly to Well A and again, a LOESS smoothing algorithm was run on oil data to interpolate and smooth out the noise. Also, oil and water rates were normalized for confidentiality. In this case, water production became negligible after the fifth day of tubing production. Only about 10% of the volume injected during the fracture treatment was recovered as water production. Gas rates were measured during the first 30 days of tubing production. It was estimated that an average GOR of 225 scf/stb. As explained in Well A it is reasonable to assume that this value will remain constant for the rest of the life of the well.

2. Flow regime identification:

Flow regimes were analyzed using Blasingame type curve (Figure 13). The first 10 days of flowback through casing data is not shown. There are four different periods shown in this plot:

I. In the first eight days an upward deviation from the -1/2 slope is seen. This is probably related to a supercharging effect due to fracture fluids injection. No bi-linear flow is observed in the early days of production.

II. Then, during the next 6 month, linear flow with -1/2 slop is seen.III. Later, during the last 5 months before the build up, a slope of -1/3 is observed. This slope could be

interpreted as a transition to pseudo radial flow, similar to the example seen in Figure 1. If this trend isconfirmed, effective permeability could be estimated from pseudo radial flow. Assuming that a pseudoradial period actually existed, Blasingame type curve matching yields an effective permeability of0.077 md (not shown in figure 13).

IV. After the buildup, there is no clear slope indication, thus more time is needed to draw furtherconclusions.

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Estimated Bottomhole Pressure

Figure 12: 18 months of production history for well B

URTeC 1965548

According to this analysis, at the moment of tnot certain if it was still in linear or pseudoradialneeded to identify flow regime.

3. Parameter extraction from straight line plots:

Figure 14a shows the linear flow chart. The linear flow parameter is estimated to be 36,316 mdacting fracture.

Figure 14b shows material balance plot.minimum OOIP of 700 Mstb. This value

First 8 days of tbg

production

Figure 13: Blasingame type curve analysis for well Bobserved. Later a pseudoradial flow may ta

Figure 14a: Linear flow chart. Liner flow is observedsince the beginning of production with a small Yintercept

at the moment of the build up, this well was producing under transient flowif it was still in linear or pseudoradial flow. Post build up data shows no clear trend an

from straight line plots:

Data follows a linear trend; no evidence of boundary dominated flow is seen.inear flow parameter is estimated to be 36,316 md05ft. The Y-intercept is small suggesting

shows material balance plot. A linear extrapolation of the last trend previous to the buildupvalue corresponds to an area of investigation of 14 acres.

6 months 5 months

-1/2 slope

-1/3 slope

Post build up data

: Blasingame type curve analysis for well B. A clear period of six months of linear flow isobserved. Later a pseudoradial flow may take place for the following five months.

Linear flow chart. Liner flow is observed since the beginning of production with a small Y-

Figure 14b: Flowing material balance chart. Linearextrapolation yields a pessimistic OOIP since boundarydominated flow has not started.

13

he build up, this well was producing under transient flow, although it is Post build up data shows no clear trend and more time is

o evidence of boundary dominated flow is seen. small suggesting a high conductivity

the buildup shows a

. A clear period of six months of linear flow is

Flowing material balance chart. Linear extrapolation yields a pessimistic OOIP since boundary

14 URTeC 1965548

4. Analytical model calibration:

As explained in Well A, multiple iterations were done to develop a robust reservoir model. The first analytical model was built assuming no pressure dependent permeability and no initial pressure increase due to fracture fluids injection (figure 15 and table 4). Similarly to well A, matching quality results are poor if both the drawdown and build up periods are considered.

A second analytical model was built, now including the effects of pressure dependent permeability (figures 16 and table 4). Although this model showed an important improvement when compared with the first run, build up period still could not be matched.

Finally, a third model was built setting initial pressure 10% higher than previous estimates. This model achieved a successful history match of both drawdown and build up periods (figure 17 and table 4). The new pressure value (0.90 psi/ft) is slightly lower than the value of fracture gradient estimated from a pump in flow back test in this well before stimulation treatment (0.92 psi/ft). This may seem inconsistent due to the small difference between poral pressure and fracture gradient. However, it is important to bear in mind that this fracture gradient value corresponds to the original state of the reservoir (before fluids injection). If poral pressure was increased due to fluids injection, then the fracture gradient should also have increased according to the stress path of the reservoir.

A more detailed analysis showed that, for this well, there is a range of pressure increase between 6% and 15% that could yield a successful history match. Similarly, it is possible to perform a history match with permeability compliance values between 2e-4 and 4.5e-4 1/psi.

0102030405060708090

100

0 2 4 6 8 10 12 14 16 18

Normalized oil rate

Month

Actual Oil Rate

Analytical Mode Oil Rate

0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18

Psi

Month

Actual BHP


0102030405060708090

100

0 2 4 6 8 10 12 14 16 18

Normalized oil rate

Month

Actual Oil Rate


0

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18

Psi

Month

Actual BHP


Figure 15: Analytical Model I history match for well B. Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). BU pressure is not properly matched with this model

Figure 16: Analytical Model II history match for well B. Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). BU pressure matching improved with this model.

15 URTeC 1965548

5. Probabilistic forecasting:

A new set of probability distributions was estimated for well B with similar considerations to well A (table 5). Effective permeability, fracture half length, conductivity and reservoir compliance were allowed to vary in each iteration to perform a history match. Similarly to well A, iterations that did not achieved a successful history match were discarded according a to a fitting error criteria. Figures 18 and 19 show the results. Figure 18 suggest a recovery factor between 3% and 6% of the SRV.

0102030405060708090

100

0 2 4 6 8 10 12 14 16 18

Month

Oil RateActual Oil Rate


0100020003000400050006000700080009000

10000

0 2 4 6 8 10 12 14 16 18

Psi

Month

Pressure

Actual BHP



Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%





Area Ininite acre

Gamma 0 1/psi


Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%





Area Ininite acre

Gamma 3.5E-04 1/psi


Porosity 6.5%

Net Pay 164 ft

Oil Saturation 70%





Area Ininite acre

Gamma 2.4E-04 1/psi

0

2

4

6

8

10

12

14

16

18

1% 2% 3% 4% 5% 6% 7% More

Fre

quen

cy


Initial Pressure Uniform 8500 9400 psi

Porosity Normal 5% 8%


Oil Saturation Normal 50% 80%

Water Saturation

Form. Compressibility Normal 5.8E-06 9.0E-06 1/psi

Reservoir Area Lognormal 14 30 Acres

100% - Oil Saturation

Figure 17: Analytical Model III history match for well B. Oil rates from sandface pressure (left) and sandface pressure from oil rates (right). Successful BU and drawdown periods with this model

Table 4: Matching parameters for models I (left), II (centre) and III (right) Well B

Table 5: Input variables for probabilistic forecast Well B Figure 18: Recovery factor histogram for well A

16 URTeC 1965548

Discussion

It was shown in the examples in Well A and B that in order to achieve a successful history match for the whole production history including both drawdown and build up periods two effects have to be accounted for:

I. Stress dependent permeability II. Reservoir average pressure higher than pre-stimulated estimates

Stress dependent permeability is documented and has been seen in other shales in the US. This effect has been widely documented in Haynesville (Clarkson et al, 2012; Ehlig-Economides and Vera, 2013; Thompson et al, 2010; Okouma et al, 2011). There are also some publications about this effect in Vaca Muerta (Fernandez Badessich and Berrios, 2012). In addition to the analysis shown in the previous section and the publications shown above, two more statements that support the pressure dependent permeability hypothesis are shown below.

Figure 20 shows permeability measurement on core plugs taken from the same wells analyzed in this paper. All the measures showed a decreasing trend in permeability when net confined stress is increased.

The second statement to support this hypothesis is the production analysis of well D. The full details are not shown so as not to make this paper so lengthy. This well flowed naturally with a BHP of about 4000 psi during the first 70 days of production. Later on, an artificial lift system was installed reducing its BHP below 1000 psi. Figure 21 shows three history matching performed with analytical models. Only model III incorporates the effect of pressure dependent permeability.

Model I was built to match the period of natural flow. According to this model, drawdown should havebeen smaller for the produced rates in the artificial lift period Model II was built matching the period of artificial lift flow. This model does not show to be able toreproduce pressures and rates during the natural flow period.

0

1

10

100

1.000

- 5 10 15 20 25

Normalized oil rate

Year

Production History

P90 forecast

P50 forecast

P10 forecast

Figure 19: Probabilistic forecast for well A

1E-06

1E-05

0,0001

0,001

0,01

0,1

1

0 1000 2000 3000 4000 5000 6000

Permeability (md)

Net Effective Stress (psi)

Figure 20: Pulse decay permeability measurement in Vaca Muerta core plugs showing a decline behavior with net effective stress.

URTeC 1965548

Finally, a third model was built including pressure dependevalue of 2e-4 1/psi was used for this modelnote that the LOESS smoothing algorithm was not run on this well, so there is more scatter in

Once it has been established that a pressurestep is to understand how this affect production forecast. To answer this question, forecasts from modelfrom well B were compared. The only difference betweeneffects of pressure dependent permeability. This analysis showed that if natural flowing (without artificial lift) isassumed for the forecast, both models yield a very similar EUR. There is only a 3% difference in favor of model I.However, if the forecast was run assuming thaextracted. Model I shows that an 86% increase in the 25 years EUR can besystem, whereas in the case of model II only a 23assumption, model I will be overestimating EUR by a 55%.

The effect of average reservoir pressure increasing in theinvestigated by the authors of this paper.following question needs to be answeraverage reservoir pressure in the SRV in aimportant to bear in mind that the fluid volume injected during the fracture treatment is anything but small orinsignificant. This volume is equivalent to the cumulative gross produ25% of the EUR of the well. Furthermore, only less than 14% of this fluid is produced back.volume is not negligible to the reservoir and it needs to be considered.simple material balance calculation was performedinjected during fracture stimulation could increasefollowing equation was used (Dake, 1978)

6 = 78699 = 6786where V is the pore volume and V the water volume injected.variables, especially pore volume; thus,simulation was run. Data from well A was used for this exercise.assumed and figure 22 the results of the

0

2000

4000

6000

8000

10000

1 61 121 181 241 301 361

PsiActual BHP


Figure 21: analytical model pressure matching from rate data (Well D). Model I (left) matches only the naturallyflowing period. Model II (centre) matches only the artificial liftpressure dependant permeability, matches both periods.

built including pressure dependent permeability effects. A reservoir compliance4 1/psi was used for this model achieving an acceptable matching of both flowing periods.

the LOESS smoothing algorithm was not run on this well, so there is more scatter in

e it has been established that a pressure dependent permeability effect is taking place in Vaca Muerta, the nextaffect production forecast. To answer this question, forecasts from model

from well B were compared. The only difference between these models was that model II had incorporatednt permeability. This analysis showed that if natural flowing (without artificial lift) is

assumed for the forecast, both models yield a very similar EUR. There is only a 3% difference in favor of model I.was run assuming that an artificial lift system was installed, opposite conclusions can be

extracted. Model I shows that an 86% increase in the 25 years EUR can be obtained by installingsystem, whereas in the case of model II only a 23% increase was obtained. This means that with the artificial lift

model I will be overestimating EUR by a 55%.

rage reservoir pressure increasing in the SRV area after the stimulation treatment wasuthors of this paper. However, little or no information was found regarding this effect. Thus, theneeds to be answered; is it physically possible that fracture injection fluids can increase

in about 15% to 25% from the original reservoir pressure?important to bear in mind that the fluid volume injected during the fracture treatment is anything but small orinsignificant. This volume is equivalent to the cumulative gross production volume of the first two years or about

Furthermore, only less than 14% of this fluid is produced back.volume is not negligible to the reservoir and it needs to be considered. To further analyze this top

material balance calculation was performed with the aim of understanding if it is possible thatcould increase the average reservoir pressure in the SRV in

(Dake, 1978) from where P can be calculated:

V the water volume injected. There is considerable uncertainty regarding; thus, a probability distribution was estimated for each of them and a

Data from well A was used for this exercise. Table 6 shows the probability distributionsof the Montecarlo simulation.

0

2000

4000

6000

8000

10000

1 61 121 181 241 301 361

PsiActual BHP


0

2000

4000

6000

8000

10000

1 61 121

Psi

analytical model pressure matching from rate data (Well D). Model I (left) matches only the naturallyflowing period. Model II (centre) matches only the artificial lift period. Model III (right), which accounts for apressure dependant permeability, matches both periods.

17

A reservoir compliance an acceptable matching of both flowing periods. Please

the LOESS smoothing algorithm was not run on this well, so there is more scatter in the data.

g place in Vaca Muerta, the next affect production forecast. To answer this question, forecasts from models I and II

was that model II had incorporated the nt permeability. This analysis showed that if natural flowing (without artificial lift) is

assumed for the forecast, both models yield a very similar EUR. There is only a 3% difference in favor of model I. installed, opposite conclusions can be

by installing an artificial lift that with the artificial lift

SRV area after the stimulation treatment was deeply regarding this effect. Thus, the

possible that fracture injection fluids can increase the pressure? As discussed, it is

important to bear in mind that the fluid volume injected during the fracture treatment is anything but small or ction volume of the first two years or about

Furthermore, only less than 14% of this fluid is produced back. Thus, the injected topic, a classical and

if it is possible that the volume in the SRV in about 20%. The

here is considerable uncertainty regarding these of them and a Montecarlo

shows the probability distributions

181 241 301 361

Actual BHP


analytical model pressure matching from rate data (Well D). Model I (left) matches only the naturally period. Model III (right), which accounts for a

18 URTeC 1965548

It can be seen from this analysis that a pressure increase between 6% and 28% over the original pressure pre-stimulation may be expected due to the fracture volume injected. It is important to remember that the pressure increase needed to obtain a history match for wells A and B lies within this range (20% and 10%).

Conclusions

Production rates and pressures from six Vaca Muerta vertical wells were analyzed with a multi-step RTA workflow. The following conclusions were drawn:

The multi-step RTA workflow proved to be a good tool for well performance analysis for Vaca Muerta wells. Good quality history matches were achieved with analytical models. All the wells showed high conductive or infinite acting hydraulic fracture stimulations. None of the wells showed evidence of boundary dominated flow. The area of investigation ranges between 6 and 14 acres for production histories between 14 and 20 months. One of the wells showed some evidence of starting a transition to pseudo-radial flow. However, more time is

needed to confirm this hypothesis. A stress dependent permeability effect seems to be taking place in Vaca Muerta. All wells analyzed needed to

include a stress dependent permeability parameter to successfully history match production. Permeabilitycompliance values may range between 1e-4 and 4.5e-4.

To successfully history match production rates, all of the wells needed to use an initial reservoir pressurebetween 10% and 20% higher than original pre-stimulation estimates.

The hypothesis that the reservoir pressure is increased due to stimulation volumes injected during fracture canbe explained by material balance. At the time of writing this paper further discussion, investigation, analysisand data gathering is being done to confirm this hypothesis.

Acknowledgements

The authors of this paper would like to thanks the Pluspetrols management for their time to review and permission to publish this paper. Special thanks to Jose Gildardo Osorio, Marcelo Pomeraniec and Debora Torchinsky for their comments and Gabriel Weber, Diego Glass, Nestor Javier Fernandez Betria, Matas Podeley, Gonzalo Cabo, Lisandro Garza and Martin Lederhos for their contributions in this work.

Nomenclature

B Fluid Volumetric Factor, b linear flow chart intercept BHP Bottom Hole Pressure C1 Constant C2 Constant ct Total Compressibility, 1/psi P Delta Pressure, psi


Wi Deterministic bbl

ct Normal 9.4E-06 1.3E-05 1/psi


Porosity Normal 5% 8% -

Reservoir Area Lognormal 9.6 30 acres

15,473

Table 6: Input parameters for material balance

Figure 22: Expected pressure increase from fracture volumes injected.

19 URTeC 1965548

V Delta Volume, ft3 EUR Estimate Ultimate Recovery Porosity, dimensionless Permeability Compliance, 1/psi h Net pay, ft ki Initial Effective Permeability, md k Effective Permeability, md LFP Linear flow parameter, md0.5ft2 m linear flow chart slope OOIP Originally Oil In Place, Mstb Pb Bubble Point Pi Initial Reservoir Pressure, psi Pwf Bottomhole Flowing pressure, psi q oil rate, stb/d Ro Vitrinite reflectance, % t Time, s Apparent Skin, dimensionless SRV Stimulated Reservoir Volume Fluid Viscosity, cp V Total Pore Volume, ft3 xf Fracture Half Length, ft

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