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Thomas Alwin Blasingame - One of the best experts on this subject based on the ideXlab platform.

  • Applicability of the Arps Rate-Time Relationships for Evaluating Decline Behavior and Ultimate Gas Recovery of Coalbed Methane Wells
    CIPC SPE Gas Technology Symposium 2008 Joint Conference, 2013
    Co-Authors: Jay Alan Rushing, Albert Duane Perego, Thomas Alwin Blasingame
    Abstract:

    This paper presents results of a simulation study designed to evaluate the applicability of Arps' [1945] decline curve methodology for assessing reserves in coalbed methane reservoirs. We simulated various coal properties and well/operational conditions to determine their impact on the production decline behavior as quantified by the Arps decline curve exponent, b. We then evaluated the simulated production with Arps' rate-time equations at specific time periods during the well's production decline period and compared estimated reserves to the "true" value (defined in this paper as the 30-year cumulative production volume). To satisfy requirements for using Arps' models, all simulations were conducted using a constant Bottomhole Flowing Pressure condition in the wellbore. The significant results from our study include: All of the computed values of the long-term decline exponents were within the limits originally defined by Arps, i.e., 0.0 < b < 1.0. Agreement between Arps' recommended b-exponent range and our results using simulated performance data also suggests that, if applied under the correct conditions, the Arps rate-time models are appropriate for assessing reserves in coalbed methane reservoirs; The Arps b-exponents were not constant during the production decline period. For many simulated cases, the early decline behavior (within a few years after reaching the peak production rate) appeared to have exponential decline but eventually became more hyperbolic later in the well's life. Use of Arps' exponential model early in the production history in those wells with long-term hyperbolic decline behavior tended to underestimate gas reserves; The largest reserve estimate errors typically occurred during the first few years after reaching the peak production rate and during the initial production decline period. For those wells exhibiting long-term hyperbolic behavior, the initial reserve estimate errors underestimated reserves by as much as 20 to 30 percent; Heterogeneities in coal properties cause the production declines to deviate from exponential to hyperbolic. Properties having the largest impact on the production decline behavior include the shape of the adsorption isotherm, cleat permeability anisotropies, the shape of cleat gas-water relative permeability curves, stress-dependent cleat permeability and porosity, and layered coal seams with differences in initial reservoir Pressures; We also observed a strong influence of well Flowing Pressure conditions as modeled with a Bottomhole Flowing Pressure constraint. For all other properties and conditions being equal, wells with lower Bottomhole Flowing Pressures exhibited more long-term hyperbolic behavior as defined by higher Arps b-exponents. Introduction Unconventional natural gas resources — tight gas sands, naturally-fractured gas shales, coalbed methane, and deep basincentered gas systems — comprise a significant percentage of our domestic natural gas resource base identified to date and represent an important source for future natural gas production and reserve growth. According to Kawata and Fujita [2001], the coalbed methane (CBM) resource-in-place in North America is estimated to total more than 3,000 Tcf. While the resource base is large, the unique gas storage and flow properties characteristic of CBM reservoirs make efficient and effective gas recovery technically difficult. Of the total resource in place, the total technically recoverable gas is estimated to be 98 Tcf. Those same unique coal properties also cause CBM production profiles to differ in shape from the production profiles for common, more conventional reservoirs. And, these differences in production profiles present unique challenges for the 2 J.A. Rushing, A.D. Perego, and T.A. Blasingame SPE 114514 accurate evaluation of reserves. The typical CBM production profile is an initial inclining production period to some peak or maximum production rate and followed by a declining rate profile. Consequently, assessing CBM reserves using the traditional Arps decline curve models and methodology is only viable during the period when the gas production is declining. However, CBM reserves may be estimated prior to the onset of a declining production profile using various reservoir models as well as numerical simulation. Regardless of the challenges posed by a typical production profile, CBM reserves are routinely assessed using the traditional Arps decline curve methodology and models. The Arps decline curve evaluation methodology consists of plotting the logarithm of production rate against time, history matching the production data using Arps' rate-time equations — i.e., the Arps exponential, hyperbolic or harmonic decline models — and extrapolating the established trend into the future. The long-term decline behavior of the extrapolated trend is typically quantified using Arps' decline exponent, b. The original Arps paper [1945], which was developed and initially applied to conventional reservoirs, indicated the b-exponent should fall between 0 and 1.0 on a semilog production plot. However, the correct b-exponent in unconventional resources like CBM reservoirs is difficult to identify correctly, particularly during the early decline period after reaching the maximum production rate. And, selection of the incorrect b-exponent will have a tremendous impact on the accuracy of CBM reserve estimates. To address these problems with reserve evaluations using the Arps models in CBM reservoirs, we have conducted a series of single-well, parametric simulation studies or "experiments" to develop a better understanding of both the shortand long-term production decline behavior and to identify those parameters affecting the production decline. For this work, our specific study objectives are: To validate the practicality and utility of the Arps rate-time relationships and decline curve methodology for estimating reserves accurately in coalbed methane reservoirs; To develop physical interpretations of Arps' b-exponents in CBM reservoirs — i.e., to determine what coal properties and operational conditions most affect the value of b; and To provide guidelines for the applicable range of b-exponents for reserve evaluations. Description of CBM Model We developed a three-dimensional, two-phase (gas-water) finite-difference model using the Computer Modeling Group's GEM (Generalized Equation-of-State Model) simulator [Reference 9]. GEM is a three-phase, multi-component compositional equation-of-state model that has been modified and adapted to capture all of the storage and flow phenomena characteristic of coalbed methane reservoirs. The model also has the capability of incorporating stress-dependent and sorption-controlled changes in coal porosity and permeability during the gas and water production process. All simulations were conducted for a single-well on a spacing of 80 aces per well. The grid system was constructed with 1521 grids (39 grids in both the xand y-directions). We employed a Cartesian grid geometry so that we could model linear flow geometry and any permeability anisotropy associated with the natural fracture or cleat system in coals. Grid dimensions in the xand y-directions were smaller immediately around the wellbore but increased geometrically away from the wellbore. Dimensions in the vertical direction ranged from three grids in the single-layer case to fifteen grids in the five-layer cases. We should note that we developed a reservoir model that addressed reservoir inflow performance, and other than using a Bottomhole Flowing Pressure constraint, we did not attempt to model well outflow performance. Productive coals are characterized by an extensive, orthogonal set of natural fractures or cleats as illustrated schematically in Fig. 1. The primary cleat system is often referred to as the "face" cleats, while the orthogonal cleats are called "butt" cleats. Typically, the face cleats are better connected and more continuous, while the butt cleats are less well connected and more discontinuous. Spacing between face cleats ranges from tenths of an inch to several inches. Interactions between the natural fracture or cleat system and the coal matrix are modeled with the dual-porosity system (Fig. 1) developed by Gilman and Kazemi [1983], where this model is modified to include all coal storage and flow processes. The majority of coal gas is stored by adsorption (i.e., gas molecules that are physically attached to the coal surfaces) rather than by "free" or unattached gas molecules stored in a matrix porosity structure similar to conventional sandstone or carbonate rocks. Most coal porosity is a combination of a micro-pore structure (pore diameters less than 2 nm) and a meso-pore structure (pore diameters between 2 and 50 nm). Because of the large surface area of the coal particles, significant volumes of gas may be stored in the adsorbed state in the microand meso-pore systems. Although it is usually not considered to be a significant contributor to either gas storage (as "free" gas that is not adsorbed)) or production (by Darcy flow), the matrix does Fig. 1 — Schematic diagram comparing actual coal cleat and matrix system with idealized dual-porosity model used in study. Actual Coal System

  • Estimating Reserves in Tight Gas Sands at HP/HT Reservoir Conditions: Use and Misuse of an Arps Decline Curve Methodology
    SPE Annual Technical Conference and Exhibition, 2007
    Co-Authors: Jay Alan Rushing, Richard Burl Sullivan, Albert Duane Perego, Thomas Alwin Blasingame
    Abstract:

    This paper presents the results of a simulation study designed to evaluate the applicability of an Arps decline curve methodology for assessing reserves in hydraulically-fractured wells completed in tight gas sands at high-Pressure/high-temperature (HP/HT) reservoir conditions. We simulated various reservoir and hydraulic-fracture properties to determine their impact on the production decline behavior as quantified by the Arps decline curve exponent, b. We then evaluated the simulated production with Arps' rate-time equations at specific time periods during the well's productive life and compared estimated reserves to the true value. To satisfy requirements for using Arps' models, all simulations were conducted using a specified constant Bottomhole Flowing Pressure condition in the wellbore. Our study indicates that the largest error source is incorrect application of Arps' decline curves during either transient flow or the transitional period between the end of transient and onset of boundary-dominated flow. During both of these periods (principally the transient period), we observed bexponents greater than one and corresponding reserve estimate errors exceeding 100 percent. The b-exponents generally approached values between 0.5 and 1.0 as flow conditions approached true boundary-dominated flow. Agreement between Arps' suggested b-exponent range and our results using simulated performance data also indicates that, if applied under the correct conditions, the Arps rate-time models are appropriate for assessing reserves in tight gas sands at HP/HT reservoir conditions. Introduction Tight gas sands constitute a significant percentage of the domestic natural gas resource base and offer tremendous potential for future reserve and production growth. According to a recent study by the Gas Technology Institute (GTI), tight gas sands in the US comprise 69 percent of gas production from all unconventional natural gas resources and account for 19 percent of total gas production from both conventional and unconventional sources. The same study estimates total domestic producible tight gas sand resources exceed 600 Tcf, while economically recoverable gas reserves are 185 Tcf. Most of the resources assessed in the 2001 GTI study were at depths less than 15,000 ft, yet the natural gas industry continues to extend exploration and development activities to much greater depths. In some geologic basins, those depths are approaching 20,000 to 25,000 ft. Many of these deep natural gas resources are not only characterized by lowpermeability, low-porosity reservoir properties, but these reservoirs also exhibit abnormally high initial pore Pressure and temperature gradients — i.e. high-Pressure/hightemperature (HP/HT) reservoir conditions. Similar to conventional natural gas resources, tight gas sand reserves are routinely assessed with Arps’ decline curve techniques. The original Arps paper suggested the decline curve exponent, b, should fall between 0 and 1.0 on a semilog plot. However, we often observe values much greater than 1.0, particularly in tight gas sands at HP/HT reservoir conditions. Deviations in observed b-exponents from the expected range suggest Arps' rate-time relationships may not be valid for modeling the decline behavior of tight gas sands at HP/HT conditions. More importantly, inappropriate use of the Arps models may cause significant reserve estimate errors in these unconventional natural gas resources. Since these depths and extreme reservoir conditions require wells that are very expensive to drill, complete and operate; it is imperative that we understand both the well productivity and production decline behavior. We also need to determine the applicability of the Arps rate-time equations for assessing reserves. To address these concerns, we have conducted a series of single-well simulation studies to develop a better understanding of both the shortand long-term production decline behavior and to identify those parameters affecting the production decline. In this study we simulated a range of reservoir and hydraulic fracture properties, including: Vertical heterogeneity from layering, permeability contrast among layers, horizontal permeability anisotropy, and stressdependent reservoir properties; 2 J.A. Rushing, A.D. Perego, R.B. Sullivan, and T.A. Blasingame SPE 109625 Variable effective fracture conductivities and lengths, unequal fracture wing lengths, two-phase and non-Darcy flow, and stress-dependent fracture properties; and Reservoir temperatures ranging from 300 to 400F and initial pore Pressure gradients ranging from 0.60 to 0.90 psi/ft. We evaluated the simulated production with the Arps ratetime equations. Reserve estimates were obtained at various time periods during the well’s productive life by extrapolating the best-fit Arps model through the simulated production. Our assumed economic conditions for estimating reserves were either a rate of 50 Mscf/d or a producing time period of 50 years, whichever came first. Reserve estimate errors were computed by comparing those estimated reserves to the “true” value. For this paper, we define the “true” estimated ultimate recovery (EUR) to be the 50-year cumulative production volume. For reference, we also summarize the Arps rate-time equations in Table 1, given below: Table 1 — Summary of the Arps' rate-time relations (Ref. 1)

  • decline curve analysis using type curves evaluation of well performance behavior in a multiwell reservoir system
    SPE Annual Technical Conference and Exhibition, 2001
    Co-Authors: Taufan Marhaendrajana, Thomas Alwin Blasingame
    Abstract:

    In this paper we present a new multiwell reservoir solution and an associated analysis methodology to analyze single well performance data in a multiwell reservoir system. The key to this approach is the use of field cumulative production data and individual well flow rate and Pressure data. Our new solution and analysis methodology couples the single well and multiwell reservoir models⎯and enables the estimation of total reservoir volume and flow properties within the drainage area of an individual well⎯with the analysis performed using a single well reservoir model (type curve). This multiwell analysis using a single well model is made possible by a coupling of the single well and multiwell solutions based on a total material balance of the system. The data required for this approach are readily available in practice: basic reservoir properties, fluid properties, well completion data, and well rate (and Pressure) data and cumulative production data for the entire field. Currently, all existing decline type curve analyses assume a single well in closed system (or single well with constant Pressure or prescribed influx at the outer boundary). In many cases a well produces in association with other wells in the same reservoir⎯and unless all wells are produced at the same constant rate or the same constant Bottomhole Flowing Pressure, nonuniform drainage systems will form during boundary-dominated flow conditions. Furthermore, it is well established that new wells “steal” reserves from older wells, and this behavior is commonly observed in the production behavior. Our new approach accounts for the entire production history of the well and the reservoir and eliminates the influence of well interference effects. This approach provides much better estimates of the in-place fluids in a multiwell system, and the methodology also provides a consistent and straightforward analysis of production data where well interference effects are observed.

  • Estimating the Stabilized Deliverability of a Gas Well Using the Rawlins and Schellhardt Method: An Analytical Approach
    SPE Eastern Regional Meeting, 1991
    Co-Authors: J L Johnston, Thomas Alwin Blasingame
    Abstract:

    duration of the shut-in tiis often is not long enough to reach the . , This paper introduces a direct method to use the results of true average reservoir Pressure in the well% drainage area. Houpeuztdeliverability analysis to derive the constants “C” and Although isochronaland modifkd isochronaltests weredevelo P “n” in the Rawlins and Schellhardt gas well deiiversbllity to circumvent the long flow times required in low permesbl ity equation. The motivation for this effort is the need to report the reservoirs, these tests may still require a single, stabili=d fiOW resultsof Rawlins and ScheUhardtanalysisto regulatory agencies, period at the end of the test in order to estimate the srrzfdfized and the widespread use of their deliverabi lity equation by producingcapacityof the well. engineers. We present a detailed procedure which shows how these results can be applied to deliverability forecasting. This The conventional deliverability test analysis technique was paper includes an ?lustmtive example in which the new nwthod is proposed by Rawlins and Schellhardt.} They observedthat a logapplied to field data from the literature. This example presents log plot of the difference between the squares of the average comparisons between Houpeurt and Rawlins and Schellhsrdt reservoir Pressure and the Bottomhole Flowing Pressure against analysesand showsthe correlation betweenthe two methods. gas flow rate can be representedby a straightline defined by qg = c G* “P;f)n . . .. . . .. . . . . . . . . , ..,.,,,,....,,, . . . . . . . . . . . .(1) INTRODUCTION The purpose of deliverability testing is to determine a gas well’s where C is defined as the stabdizai performancecoefficient,and n is the reciprocal of the slope of the straight line, Extrapolation of production capabilities under’specific reservoir conditions. A this line to the difference between the squares of ths average common prmiuctivity indicator obtained from these tests is the reservoir Pressure and the Bottomhole Flowing Pressure equal to absolute open flow (AOF) potential, which is defined as the atmosphericPressuredefines the AOF. maximum rate at which a well could flow against a theoretical atmospheric backPressure at the sandface. Although in practice Eq, 1 was developed empirically from the observation of a the well cannot produce at this rate, the AOF is often used by number of gas well tests. Extrapolation of Eq. 1 over large regulatory agencies for establis%tg field proration schedulesand variationsin pmssum can result in incorrectestimatesof the AOF, settingmaximumallowableproductionrates for individualwells. Subsequent theoretical developments by Houpeurd have shown A number of testing techniques have been developed to assess a that a more accurateanalysisfor gas flow is possiblewith gas well’sdeliverabilitycharacteristics, Flow+fter-flowl tests am P2-p~f=aq8+bqi ,,, ., .. . . . . . . . . . . . .. . .. . . . . .. . . . ,, .,4,.. (2) conducted by producin$ the well at a seriesof different flow rates and measuring the stabdized Bottomholeflowin$ Pressures, Each where the flowcoefficients,a and b, are defined by flowrate is establishedin successionwithoutan intermediateshutin period. The primary limitation of these tests is the long time a= L422x106~ Z T required to reach stabilization in low permeability reservoirs. Consequently, the isochrorta12and modified isochronal tests k,ll g ‘ [’151’~g(%)-i+’] .b.,(3, were developed to shotten test times. ~ = L422x106 jit Et TD kt h An isochronal testis conductedby alternativelyproducing the .,, ,!,.,.,.,,, ,,, ,,, ,,, ,,, ,, ., .,... (4) well, then shutting it in md allowing it to build up to the average 7 2 is a solution to the diffusivity equation for radial flow, reservoir ressure prior to the beginning of the next flow period. R A ;hough t$e Houpeurt equation has a theoretical basis and is The rnodi ed isochronal test is conducted similsriy, except the rigorously correct, the more famiiiw but em ifically b~ed Rawlins and Schellhardt equation continues to L used, inkd Referencesm~Uustradons at end of paper favored, b I the natural gas industry. Consequently, we have i combined e two analysistechn4uea and havedevelopeda mm 2 Estimatingthe Stabtized Deliverabilityof a Gss WellUsing the SPE 23440 Rawtinsand SchellhardtMethod: An AnalyticalAppioach . accurate version of the Rswlins-Schellhardtmethod which can be used in deliverability forecasting. Our technique can be used to estimate the stabihzed performance coefficient, C, without requiring stabilizedFlowingconditions and is especiallyuseful for analyzing isochronal and modified isochronal tests without a stabilized flow period. This is a simple method which rquires only data from a modified isochronal test to develop a performance prediction for the well without a priori estimates of reservoir properties. I?ZFINITIONS AND THEORETICAL DEVELOPMENT Our deliverability test analysis technique is derived by equating derivatives of log (qg) with respect to log (AP ) from both the Rawlins-Schellhardt and Houpeurt equations, 1 similar method was used by Brigham,s Duong,s and Poettmann and KazemiTto develop quations in terms of Pressures-squared for estimating reservoir properties from gas deliverability tests. Because of the Pressure-dependentgas properties, the Pressure-squaredforms of the deliverability equations (Eqs, 1 and 2) often are inaccurate at high Pressures. Therefore, in the subsquent derivation, we use the pseudoPressure transformation introduced by A1-Hussainy,et al,a J P pp=2 P& pb /404 ‘go) . . . . . . . . . . . . ! ..,...,.,,, . . . . . . . . . . . . . . .(5) Our method is a!su applicable, however, for deliverability quations written with pnssure-squared as the dependentvariable. In terms of pseudoPressure,the Rawlins and Schellhardtequation becomes qg = C ~P@) PP@wf)l” %’ ,,, ., .,,.,,... . . . . . . . . . . . . . . . . . . . . . . .(6) Similarly, the Houpeurtquation is Pp@) PP(IW) = a qg + b q~ . . . . . .. . . . . .. . .. . .. . . . . . .. . .. ...(7) where the flowcoefficients,u and b, are defined by 6 b ~x10 = D kg h , .,, ., ...,..,, . . . . . . . . . . . . . . ,.! ...,,,0,,,, (9) Taking the logarithmof both sidesof Eq, 6 yields /og(q8) =/og(C)+ fl/og~~@ -PP@w/)] .. ...0.. . .. .. ..(lO) Reatmnging Eq. 10 and solving for n shows that n is the slope of a log-log plot of qg vs. *P Alternatively, n can be expressed as the derivativeof log (q8) with respect to log (A@: ‘=m&3i$m=hm#kz-T”( ll) Similarly, taking the logaxithmof both sides of E@7 yields ‘OtiP@-pp@~j)] =10~aq8+ bql]... !...!..! . .. ..!. (12) Differentiatinglog (@P) with respect to q, gives . a+2bq r ,,..,.,.,,,..,.,,,,,., (13) aqg+bq~ Substituting F@ 13 into Eq 11 yields an quation for n in terms of the Houpeurtflow eoefftciem [1 ~=1 ~g+%l -a+bg qg a+2bqg (14) a+2bqg To develop an expression for the performance coefficient, C, in terms of the Houpeurt flow coefficients,we combine Eqs, 10 and 12to obtain In c‘(a~,~bqf~ = (a f~qg)” . . . . . . . . . . . . . . , ...,,...,,, (15) Eq. 15 is similar to a result derived by Poettmann and Kazemi.T We show the applications and importance of this development in the procedure in the next section of this paper, The flow rate required in Eq, 15 is defined by solving Eq. 14 for the gas flowrate, qg, a(l -n) ‘g= b(2n-1) .. ... .. . . . . . .. . .. . . . . . . . .. . . .. . .. . . . . .. . ... ... (16) Implicit in our derivation is the assumption of radial flow of a single-phase gas in a homogeneous, isotropic reservoir, For naturally fractured reservoirs, our method, like conventional deliverability analysis techniques, is valid only after the matrixfracture system has begun to behave like a single, homogeneous unit. Similarly, our method is valid only after pseudoradial flow is exhibited in hydraulically fractured wells, We also assume wellborestorageeffects are negligible. I“ DELIVERABILITY TEST ANALYSIS Application of our method assumes that the slo~, l/n, of the empirical deliverability plot remains constant wnh time. This assumption implies that, if we can calculate values of a and b (I@. 8 and 9, respectively) for given reservoir properties, we also can calculatea flow rate with@. 16, We then substitute this flow rate into Eq. 15 and calculate a stabilized C value, and assuminga constantvalue for n, calculatethe AOF AOF = C ~p(ji) Pp@b)r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (17) To apply our new deliverability analysis technique to field data, we present an analysis procedure below. We will then apply this procedureto a fieldexample, General Analysis Procedure. We recommend the following proceduro to analyze isochronal and modified isochronal tests using our technique, Although presented in terms of pseudoPressures, this procedure also is applicable with the Pressure-squaredvariables, 1. Plot 4P = Pp@) Pp@wf) vs. qg on log-log graph paper for the measuredflow data. 2, For each flow time, construct the best tit line through the data points, Typically, some of the earl data points will not J’ agree with the general trend of the ata, so these points should be ignored in all subsequentanalyses. 3. Determine the deliverability exponent, n, for each best-fit line by least-squaresregressionanalysis using the following equation: I !i1OW.5(10,4P)J N$(fOgq810g&p~-, , ‘=-$=’’”(’8) 4, For M isochronal tests, compute the arithmeti~ average deliverabilityexportcn4ii,

Amin Ettehadtavakkol - One of the best experts on this subject based on the ideXlab platform.

  • Transient shale gas flow model
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Dian Fan, Amin Ettehadtavakkol
    Abstract:

    Abstract This paper develops a new transient shale gas flow (TSGF) model specifically tuned for typical hydraulically-fractured horizontal gas wells and addresses the role of fracture-network conductivity in the evaluation of unconventional reservoir performance. This flow model includes the desorption effect and is analytically developed to derive the dimensionless Pressure response to the primary depletion phase in shale gas reservoirs. The TSGF model captures the linear flow in the hydraulic fracture and the stimulated reservoir volume under the effects of well geometry, multiple reservoir and hydraulic-fracture properties, and well operating conditions. The unique feature of the unconventional shale gas wells is the simultaneous decline of average reservoir Pressure, production rate, and Bottomhole Flowing Pressure with an ultimate Bottomhole Pressure constraint. This important feature is analytically investigated with the TSGF model. Other Pressure-dependent reservoir characteristics such as gas compressibility, viscosity, formation volume factor, and adsorbed gas density are included in the TSGF model. The TSGF model is validated through the history-matching and predictions of Haynesville and Marcellus monthly flowrate data for about 20 wells. The model yields appropriate estimations of well and reservoir properties and predicts the estimated ultimate recovery (EUR) in the multiple hydraulically-fractured horizontal wells. Compared to the empirical stretched exponential production decline (SEPD) and power-law exponential (PLE) models, the TSGF model shows similar flowrate predictions at late-production times. The results also show that given a minimum 12 months of production data, the TSGF model robustly predicts the well performance and the EUR for typical shale gas reservoirs.

David R. Childers - One of the best experts on this subject based on the ideXlab platform.

  • Forecasting shale gas performance using the Connected Reservoir Storage Model
    Journal of Natural Gas Science and Engineering, 2020
    Co-Authors: David R. Childers
    Abstract:

    Abstract Characterized by rock sediments and complicated storage mechanisms, shale reservoir development is to create flow systems comprised of complex natural fissures and hydraulic fracture stimulation such that the hydrocarbons in the reservoir can be recovered economically. The prediction of shale reservoir production performance is critical for reservoir management and challenging due to the compounding nature of multiple flow mechanisms one encounters during the life of a shale gas well. Given empirical nature, many models based on curve-fitting historical production data prone to inaccurately forecast well's rates in shale gas reservoirs, particularly when the production history of a well is short. This paper further extends the applicability of the Connected Reservoir Storage Model (CRSM) to shale reservoirs. The CRSM was derived from diffusivity theory by using historical production rate and Bottomhole Flowing Pressure (BHFP) to determine the reservoir behavior and unit Pressure response, and it can be conveniently used to predict production performance under variable operating conditions. The CRSM is mathematically robust and easy for application since it can be determined with limited reservoir information without knowing the reservoir geometry or reservoir permeability. This study uses the operational history of a shale gas well to demonstrate the CRSM's capability in rate prediction and couples the model with stochastic methods to approximate the most probable outcome under variable operating conditions.

Dian Fan - One of the best experts on this subject based on the ideXlab platform.

  • Transient shale gas flow model
    Journal of Natural Gas Science and Engineering, 2016
    Co-Authors: Dian Fan, Amin Ettehadtavakkol
    Abstract:

    Abstract This paper develops a new transient shale gas flow (TSGF) model specifically tuned for typical hydraulically-fractured horizontal gas wells and addresses the role of fracture-network conductivity in the evaluation of unconventional reservoir performance. This flow model includes the desorption effect and is analytically developed to derive the dimensionless Pressure response to the primary depletion phase in shale gas reservoirs. The TSGF model captures the linear flow in the hydraulic fracture and the stimulated reservoir volume under the effects of well geometry, multiple reservoir and hydraulic-fracture properties, and well operating conditions. The unique feature of the unconventional shale gas wells is the simultaneous decline of average reservoir Pressure, production rate, and Bottomhole Flowing Pressure with an ultimate Bottomhole Pressure constraint. This important feature is analytically investigated with the TSGF model. Other Pressure-dependent reservoir characteristics such as gas compressibility, viscosity, formation volume factor, and adsorbed gas density are included in the TSGF model. The TSGF model is validated through the history-matching and predictions of Haynesville and Marcellus monthly flowrate data for about 20 wells. The model yields appropriate estimations of well and reservoir properties and predicts the estimated ultimate recovery (EUR) in the multiple hydraulically-fractured horizontal wells. Compared to the empirical stretched exponential production decline (SEPD) and power-law exponential (PLE) models, the TSGF model shows similar flowrate predictions at late-production times. The results also show that given a minimum 12 months of production data, the TSGF model robustly predicts the well performance and the EUR for typical shale gas reservoirs.

Ahmed H. El-banbi - One of the best experts on this subject based on the ideXlab platform.

  • Effects of production, PVT and pipe roughness on multiphase flow correlations in gas wells
    Journal of Petroleum Exploration and Production Technology, 2020
    Co-Authors: Mohamed A. Abd El-moniem, Ahmed H. El-banbi
    Abstract:

    The importance of gas production has increased as gas represents a clean source of energy. We studied different multiphase flow correlations for gas wells. We collected large database for Bottomhole Flowing Pressure for different flow conditions and well configurations. In total, 32 gas wells were selected and our target was to study the effect of multiphase flow correlations input parameters on the accuracy of the predicted Pressure drop. Several important multiphase correlations input parameters were selected for this study. These include condensate to gas ratio (CGR) and water to gas ratio (WGR) which represent the production conditions, API and specific gravity of surface gas ( Ɣ _g) which represent PVT properties and the tubing roughness ( ε ) which represents the tubing condition. Our method was based on changing the values of these selected parameters by a percentage from its original value and determining the new predicted Bottomhole Flowing Pressure. Consequently, we determined the new error compared to the actual measured Bottomhole Pressure. We performed 352 cases, and we could obtain the effect of the different parameters on both Pressure drop calculations and the selection of the best correlation. Guidelines were developed to explain which parameters are more important to be measured accurately for different conditions.

  • Effects of production, PVT and pipe roughness on multiphase flow correlations in gas wells
    Journal of Petroleum Exploration and Production Technology, 2020
    Co-Authors: Mohamed A. Abd El-moniem, Ahmed H. El-banbi
    Abstract:

    The importance of gas production has increased as gas represents a clean source of energy. We studied different multiphase flow correlations for gas wells. We collected large database for Bottomhole Flowing Pressure for different flow conditions and well configurations. In total, 32 gas wells were selected and our target was to study the effect of multiphase flow correlations input parameters on the accuracy of the predicted Pressure drop. Several important multiphase correlations input parameters were selected for this study. These include condensate to gas ratio (CGR) and water to gas ratio (WGR) which represent the production conditions, API and specific gravity of surface gas (Ɣg) which represent PVT properties and the tubing roughness (e) which represents the tubing condition. Our method was based on changing the values of these selected parameters by a percentage from its original value and determining the new predicted Bottomhole Flowing Pressure. Consequently, we determined the new error compared to the actual measured Bottomhole Pressure. We performed 352 cases, and we could obtain the effect of the different parameters on both Pressure drop calculations and the selection of the best correlation. Guidelines were developed to explain which parameters are more important to be measured accurately for different conditions.

  • An Integrated Model for History Matching and Predicting Reservoir Performance of Gas/Condensate Wells
    SPE Reservoir Evaluation & Engineering, 2013
    Co-Authors: A.m.. M. Farid, Ahmed H. El-banbi, A.a.. A. Abdelwaly
    Abstract:

    Summary The depletion performance of gas/condensate reservoirs is highly influenced by changes in fluid composition below the dewpoint. The long-term prediction of condensate/gas reservoir behavior is therefore difficult because of the complexity of both composition variation and two-phase-flow effects. In this paper, an integrated model was developed to simulate gas-condensate reservoir/well behavior. The model couples the compositional material balance or the generalized material-balance equations for reservoir behavior, the two-phase pseudo integral Pressure for near-wellbore behavior, and outflow correlations for wellbore behavior. An optimization algorithm was also used with the integrated model so it can be used in history-matching mode to estimate original gas in place (OGIP), original oil in place (OOIP), and productivity-index (PI) parameters for gas/condensate wells. The model also can be used to predict the production performance for variable tubinghead Pressure (THP) and variable production rate. The model runs fast and requires minimal input. The developed model was validated by use of different simulation cases generated with a commercial compositional reservoir simulator for a variety of reservoir and well conditions. The results show a good agreement between the simulation cases and the integrated model. After validating the integrated model against the simulated cases, the model was used to analyze production data for a rich-gas/condensate field (initial condensate/gas ratio of 180 bbl/ MMscf). THP data for four wells were used along with basic reservoir and production data to obtain original fluids in place and PIs of the wells. The estimated parameters were then used to forecast the gas and condensate production above and below the dewpoint. The model is also capable of predicting reservoir Pressure, Bottomhole Flowing Pressure, and THP and can account for completion changes when they occur.