Package: petbox-dca
Empirical analysis of production data requires implementation of several decline curve models spread over years and multiple SPE publications. Additionally, comprehensive analysis requires graphical analysis among multiple diagnostics plots and their respective plotting functions. While each model’sq(t)(rate) function may be simple, theN(t)(cumulative volume) may not be. For example, the hyperbolic model has three different forms (hyperbolic, harmonic, exponential), and this is complicated by potentially multiple segments, each of which must be continuous in the rate derivatives. Or, as in the case of the Power-Law Exponential model, theN(t)function must be numerically evaluated.This library defines a single interface to each of the implemented decline curve models. Each model has validation checks for parameter values and provides simple-to-use methods for evaluating arrays oftimeto obtain the desired function output.Additionally, we also define an interface to attach a GOR/CGR yield function to any primary phase model. We can then obtain the outputs for the secondary phase as easily as the primary phase.Analytic functions are implemented wherever possible. When not possible, numerical evaluations are performed usingscipy.integrate.fixed_quad. Given that most of the functions of interest that must be numerically evaluated are monotonic, this generally works well.Primary PhaseTransient Hyperbolic,Modified Hyperbolic,Power-Law Exponential,Stretched Exponential,DuongSecondary PhasePower-Law YieldWater PhasePower-Law YieldThe following functions are exposed for useBase Functionsrate(t),cum(t),D(t),beta(t),b(t),Transient Hyperbolictransient_rate(t),transient_cum(t),transient_D(t),transient_beta(t),transient_b(t)Primary Phaseadd_secondary,add_waterSecondary Phasegor(t),cgr(t)Water Phasewor(t)Utilitybourdet,get_time,get_time_monthly_volGetting StartedInstall the library withpip:pipinstallpetbox-dcaA default time array of evenly-logspaced values over 5 log cycles is provided as a convenience.>>>frompetboximportdca>>>t=dca.get_time()>>>mh=dca.MH(qi=1000.0,Di=0.8,bi=1.8,Dterm=0.08)>>>mh.rate(t)array([986.738,982.789,977.692,...,0.000])We can also attach secondary phase and water phase models, and evaluate the rate just as easily.>>>mh.add_secondary(dca.PLYield(c=1200.0,m0=0.0,m=0.6,t0=180.0,min=None,max=20_000.0))>>>mh.secondary.rate(t)array([1184.086,1179.346,1173.231,...,0.000])>>>mh.add_water(dca.PLYield(c=2.0,m0=0.0,m=0.1,t0=90.0,min=None,max=10.0))>>>mh.water.rate(t)array([1.950,1.935,1.917,...,0.000])Once instantiated, the same functions and process for attaching a secondary phase work for any model.>>>thm=dca.THM(qi=1000.0,Di=0.8,bi=2.0,bf=0.8,telf=30.0,bterm=0.03,tterm=10.0)>>>thm.rate(t)array([968.681,959.741,948.451,...,0.000])>>>thm.add_secondary(dca.PLYield(c=1200.0,m0=0.0,m=0.6,t0=180.0,min=None,max=20_000.0))>>>thm.secondary.rate(t)array([1162.417,1151.690,1138.141,...,0.000])>>>ple=dca.PLE(qi=1000.0,Di=0.1,Dinf=0.00001,n=0.5)>>>ple.rate(t)array([904.828,892.092,877.768,...,0.000])>>>ple.add_secondary(dca.PLYield(c=1200.0,m0=0.0,m=0.6,t0=180.0,min=None,max=20_000.0))>>>ple.secondary.rate(t)array([1085.794,1070.510,1053.322,...,0.000])Applying the above, we can easily evaluate each model against a data set.>>>importmatplotlib.pyplotasplt>>>fig=plt.figure()>>>ax1=fig.add_subplot(121)>>>ax2=fig.add_subplot(122)>>>ax1.plot(t_data,rate_data,'o')>>>ax2.plot(t_data,cum_data,'o')>>>ax1.plot(t,thm.rate(t))>>>ax2.plot(t,thm.cum(t)*cum_data[-1]/thm.cum(t_data[-1]))# normalization>>>ax1.plot(t,ple.rate(t))>>>ax2.plot(t,ple.cum(t)*cum_data[-1]/ple.cum(t_data[-1]))# normalization>>>...>>>plt.show()See theAPI documentationfor a complete listing, detailed use examples, and model comparison.Developmentpetbox-dcais maintained by David S. Fulford (@dsfulf). Please post an issue or pull request in this repo for any problems or suggestions!Version History1.0.6New functionsAddedWaterPhaseclassAddedWaterPhase.wormethodAddedPrimaryPhase.add_watermethodOther ChangesAyieldmodel may inherit bothSecondaryPhaseandWaterPhase, with the respective methods removed upon attachment to aPrimaryPhase.1.0.5New functionsBourdet algorithmOther ChangesUpdate docstringsAdd bourdet data derivatives to detailed use examples1.0.4Fix typos in docs1.0.3Add documentationGenericize numerical integrationVarious refactoring0.0.1 - 1.0.2Internal releases