碳酸盐岩储层基质酸化或压裂酸刺激方法的决策标准

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SPE-199236-MS

Decision Criterion for Acid Stimulation Method in CarbonateReservoirs: Matrix Acidizing or Acid Fracturing?

Mateus Palharini Schwalbert, Petrobras; Murtada Saleh Aljawad, King Fahd University of Petroleum and Minerals;Alfred Daniel Hill and Ding Zhu, Texas A&M University

This paper was prepared for presentation at the SPE International Conference and Exhibition on Formation Damage Control held in Lafayette, Louisiana, USA, 19-21February 2020.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contentsof the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflectany position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the writtenconsent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations maynot be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

Most wells in carbonate reservoirs are stimulated. Due to low cost and simpler operations, acid stimulationmethods are usually preferred if they are sufficient. Matrix acidizing can effectively stimulate carbonatereservoirs, often resulting in skin factors on the order of -3 to -4. In low confining stress and hard rocks,acid fracturing can yield better results than matrix acidizing. However, acid fracturing is less effectivein high permeability, high confining stress, or soft rocks. There is a combination of parameters, amongthem permeability, confining stress, and rock geomechanical properties, that can be used as a criterion todecide between matrix acidizing and acid fracturing which is the best acid stimulation technique for a givenscenario.

This study compares the productivity of matrix acidized and acid fractured wells in carbonate reservoirs.The criterion used to decide the preferred method is the largest productivity obtained using the same volumeof acid for both operations. The productivity of the acid fractured wells is estimated using a fully-coupledacid fracturing simulator, which integrates the geomechanics (fracture propagation), pad and acid transport,heat transfer, temperature effect on reaction rate, effect of wormhole propagation on acid leakoff, and finallycalculates the well productivity by simulating the flow in the reservoir towards the acid fracture. Using thissimulator, the acid fracturing operation is optimized, resulting in the operational conditions (injection rate,type of fluid, amount of pad, etc) that lead to the best possible acid fracture that can be created with a givenamount of acid. The productivity of the matrix acidized wells is estimated using the most recent wormholepropagation models upscaled to field conditions.

Results are presented for different types of rock and reservoir scenarios, such as shallow and deepreservoirs, soft and hard limestones, chalks, and dolomites. For each type of reservoir rock and confiningstress, there is a cut-off permeability below which acid fracturing results in a more productive well;above this cut-off permeability, matrix acidizing should be preferred. This result agrees with the generalindustry practice, and the estimated productivity agrees with the results obtained in the field. However,the value of the cut-off permeability changes for each case, and simple equations for calculating it arepresented. For example, for harder rocks or shallower reservoirs, acid fracturing is more efficient up tohigher permeabilities than in softer rockers or at deeper depths.

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This method provides an engineered criterion to decide the best acid stimulation method for a givencarbonate reservoir. The decision criterion is presented for several different scenarios. A simplified conciseanalytical decision criterion is also presented: a single dimensionless number that incorporates all pertinentreservoir properties and determines which stimulation method yields the most productive well, withoutneeding any simulations.

Introduction

A great part of the world's conventional hydrocarbon reserves are found in carbonate reservoirs. The rocksthat form these reservoirs are composed of more than 50% of carbonate minerals (Economides and Nolte,2000), the most common being calcite (CaCO3) and dolomite (CaMg(CO3)2). Most wells in these reservoirsare stimulated.

The most common stimulation methods applied in this scenario are: matrix acidizing, acid fracturing,and propped hydraulic fracturing (Economides and Nolte, 2000). The first two methods take advantage ofthe fact that carbonate rocks are soluble in most acids.

From an operational point of view, the execution of an acid fracturing treatment is easier than theexecution of a propped fracture (Economides and Nolte, 2000), due mainly to the risk of screenouts inpropped hydraulic fracturing. It has been reported that the propped hydraulic fracture is difficult to beconcluded in hard offshore carbonates with high closure stresses due to screenouts (Neumann et al., 2012,Azevedo et al., 2010). The stability of the rock layers above and below the reservoir when subjected to thehigh pressure of the fracturing process is also an operational concern (Oliveira et al., 2014), as well as theintegrity of wellbore equipment.

Due to low cost and simpler operations, if acid stimulation methods are sufficient to stimulate a givenwell, they are usually preferred instead of propped hydraulic fracturing. Especially in offshore wells, whereoperational problems lead to more costly consequences, the methods that offer less risk are usually preferred.In the stimulation of wells in carbonate reservoirs, if matrix acidizing or acid fracturing can give resultssimilar to the propped hydraulic fracturing, the first two methods are usually preferred for practical reasons.There are studies regarding selection of the hydraulic fracturing method for a given scenario (selectingbetween acid and propped fracture). Examples of such studies are Ben-Naceur and Economides (1988),Abass et al. (2006), Vos et al. (2007), Azevedo et al. (2010), Neumann et al. (2012), Oliveira et al. (2014),Jeon et al. (2016), Suleimenova et al. (2016), and Cash et al. (2016).

Daneshy et al. (1998) mention that proppant is usually required in wells with closure stress greater than5,000 psi. However, Neumann et al. (2012) discuss the fact that the limit of 5,000 psi is just a generalguideline based on the behavior of shallow soft carbonates, while deeper carbonates may be mechanicallymore competent in some cases. Neumann et al. (2012) and Oliveira et al. (2014) present some results thatmay expand the limit of 5,000 psi to higher values.

However, there has not been much study regarding the selection of the stimulation method betweenmatrix acidizing and acid fracturing. Oliveira et al. (2014) reported problems and unsatisfactory results inacid fracturing operations when a matrix acidizing operation had already been performed on the same well.They mention the importance of a criterion to select the best stimulation method between acid fracturingand matrix acidizing, which is not obvious and does not yet exist in the industry.

The focus of this study is on matrix acidizing and acid fracturing in carbonate reservoirs. In bothtechniques, the enhancement in well performance results from a dissolution structure created by acid, andthe outcome is somewhat proportional to the volume of acid injected. So it is expected that, for a given welland volume of acid, one of these methods renders better results than the other.

A well designed and executed matrix acidizing treatment can result in a skin factor on the order of -4(Burton et al., 2018). This result depends on the reservoir mineralogy and existence of natural fractures, but

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it seems to be insensitive to reservoir permeability, according to a large dataset presented by Burton et al.(2018), as long as the reservoir is permeable enough to allow matrix injection.

The outcome of acid fracturing operations, however, is very sensitive to reservoir permeability. In general,fracturing stimulation results in better (more negative) skin factor when performed in reservoirs of lowerpermeability. This leads to the industry general rule of thumb: to matrix acidize wells in carbonate reservoirsof high permeability, and to acid fracture (or hydraulic fracture in general) wells in carbonates of lowerpermeability. The value of the cutoff permeability, however, is not clearly defined in the literature, anddifferent companies use different cutoff values. Daneshy et al. (1998) mention that a general guideline isto use acid fracturing in carbonate reservoirs with permeability smaller than 20md. However, they do notpresent a source or a scientific reason for this choice of value.

The objective of this study is to define a decision criterion to select the best method for a given scenario,between matrix acidizing and acid fracturing. In other words, for a given scenario and volume of acid, isit preferable to matrix acidize or acid fracture a well?

In practice this decision does not depend only on the achievable productivity index. For example, in wellswhere zonal isolation is important, if the geomechanics indicates that a hydraulic fracture can grow intoundesired zones, it is common to avoid hydraulic fracturing. Because the pressures involved in fracturing arehigher than in matrix acidizing, there may be also mechanical and logistical constraints to using hydraulicfracturing. In addition, matrix acidizing is a simpler stimulation method, with low risk of failure, low cost,and longstanding results (as shown in Burton et al., 2018). In this sense, if the maximum productivity indexis not a concern, matrix acidizing is often the selected method.

However, mechanical or logistical constraints are not analyzed in this study. This work focuses on wellswhere both methods can be applied, with the objective of determining which method has potential to resultin greater productivity index using the same volume of acid. Other stimulation methods, such as proppedhydraulic fracturing, are not included in the analysis.

For the fractured wells, only the production from the bi-wing fracture is considered. No natural fracturenetworks are considered. In Ugursal et al. (2018), we studied the productivity of acid fractured wells wherethe main acid fracture intersects natural fractures. As a general rule, in all cases presented in Ugursal etal. (2018), the productivity index with or without the presence of natural fractures is on the same order ofmagnitude. In most cases, the productivity is larger when the hydraulic fracture intersects natural fractures,but in some cases it is lower because of the acid lost to the natural fractures. As the study did not consider thefracture propagation, those results should ideally be revisited with a fracturing model that includes fracturepropagation.

In this section, only the productivity index in the pseudo-steady state is used for comparison. The same hasbeen applied for most studies of productivity of conventional hydraulic fractured wells, such as Economideset al. (2002) and Meyer and Jacot (2005), and it is a consensus that optimizing the productivity index for thepseudo-steady state is enough for conventional reservoirs. In fact, Economides et al. (2002) mention that a\flow period does not change the previous conclusions on optimal dimensions. Our calculations show thatthere is no reason to depart from the optimum compromise derived for the pseudo-steady state case, evenif the well will produce in the transient regime for a considerable time (say months or even years). Simplystated, what is good for maximizing pseudo-steady state flow is also good for maximizing transient flow\Summarizing, the objective of this study is to create a decision criterion to estimate, for a given scenarioand volume of acid, which stimulation technique can result in the higher productivity index in the pseudo-steady state: matrix acidizing or acid fracturing?

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Methodology

The methodology used in this study consisted of using state of the art models to simulate wormholepropagation and acid fracturing operations, and to calculate the dimensionless productivity indices of thestimulated wells. The whole modeling and analysis are detailed in Palharini Schwalbert (2019).The dimensionless productivity index used in this study is defined, in consistent units, as:

(1)

where JD is the dimensionless productivity index, q is the production rate, Δpreservoir is the reservoirdrawdown, B is formation volume factor of the produced fluid, μ is the produced fluid viscosity, kH is theeffective horizontal permeability, h is the net pay (permeable formation thickness), and J is the productivityindex (q/Δpreservoir). The same equation applies for injector wells, in which case q is injection rate and thefluid properties are those of the injected fluid. The same definition of JD was used for both vertical andhorizontal wells.

The model presented by Al Jawad (2018) was used to simulate the acid fracturing operations, withthe modifications presented in Palharini Schwalbert (2019). The acid fractured well productivity modelpresented in Aljawad et al. (2018) was used to evaluate the productivity of the fractured wells. Basically,the fully coupled model simulates the fracture propagation and dissolution of the fracture walls due to acidreaction. The fracture conductivity is calculated using correlations such as Nierode and Kruk (1973) or Denget al. (2012). The temperature distribution is also solved at each time step, and it is coupled with the acidtransport and reaction, which is especially significant in the simulation of dolomite formations, as shown inAljawad et al. (2019). The fractured well productivity is calculated by simulating the flow in the reservoirdrainage region of the given well.

The optimization of the acid fracturing operations followed the procedure presented in Aljawad et al.(2018). The optimal acid fracturing design for each scenario is found by simulating different operationalconditions (varying injection rate, type of acid system, and amount of pad), until a maximum productivityindex is achieved for a given amount of acid employed.

In the case studies presented in this study, the acid fracture conductivity model by Nierode and Kruk(1973) was used. The results obtained, however, do not apply only to that model. They can be generalized toother conductivity models using the generalized conductivity correlation presented in Palharini Schwalbert(2019).

The matrix acidizing operations were simulated using global models of wormhole propagation. Themodel used in the cases presented in this paper was the one presented in Palharini Schwalbert et al. (2019b).Palharini Schwalbert (2019) also presents comparison with the models by Buijse and Glasbergen (2005)and Furui et al. (2010). The skin factor of the acidized well was calculated using either Hawkins’ formulaor the equations presented by Palharini Schwalbert et al. (2019a).

The properties of the reservoir, well, formation, and acid used as the base case are presented in Table 1.All the cases are built using the properties listed in Table 1, varying one or more properties case to case. Thereaction kinetics parameters and heat of reaction were obtained from Schechter (1992), and are presentedin Table 2. Three different acid systems were considered: straight, gelled, and emulsified acid. These acidsystems may have different properties, depending on the chemical additives types and concentration. Theproperties used for each acid system in this study are presented in Table 3, and are considered representativeof a reactive acid system (straight acid), retarded system (gelled acid), and very retarded system (emulsifiedacid).

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Table 1—input data used for the base caseInput Data

Reservoir/Formation Properties

Reservoir permeability, kReservoir porosity, ?

Reservoir initial pressure gradient

Bottomhole flowing pressure gradient (during production)Minimum horizontal stress gradientBreakdown pressure gradientBiot poroelastic coefficientPoisson ratioYoung's ModulusToughness, KIc

Rock Embedment Strength, SRESMineralogy

Formation fluid density, ρfReservoir thickness (net pay), hDrainage region length in x-direction, LxDrainage region length in y-direction, LyFormation fluid viscosity, μfFormation volume factor, BTotal compressibility, ctReservoir temperature, TRFormation rock density, ρma

Formation specific heat capacity, cmaFormation thermal conductivity, kma

Wellbore Properties

Vertical wellWellbore radius, rwInner casing radius, r1Outer casing radius, r2

Overall heat transfer coefficient, UtAmbient temperature, Tb

Mechanical Properties of Layers Above and Below Pay Zone

Poisson ratioYoung's ModulusToughness, KIcHorizontal Stress

Acid Properties

Density, ρ

Acid initial concentration, Ci

67 lbm/ft3

15%0.254×106 psi2200 psi-inch0.5

400psi above reservoir's stress