Decline Curve Analysis Methods Research Articles

Fast Optimization of the Net Present Value of Unconventional Wells Using Rapid Rate-Transient Analysis

Summary Decline-curve analysis (DCA) is the industry standard to predict hydrocarbon production to then estimate the net present value (NPV) of unconventional wells. Conventional DCA assumes the flowing bottomhole pressure (BHP) is constant. This is an unrealistic assumption for many unconventional wells that can lead to incorrect estimates of ultimate recovery and thus erroneous NPV calculations. This work illustrates the application of a novel technique that combines variable BHP conditions with decline-curve models [Rapid rate-transient analysis (RTA)] to estimate the NPV and select the optimal cluster spacing and number of fractures for a given well. We compare the results of DCA and Rapid RTA to estimate and optimize the cluster spacing for a tight oil and shale gas well. The Rapid RTA results show marked differences in terms of the shape and the optimal value of the NPV function from the simpler DCA method. For the two cases illustrated, DCA results are a direct consequence of the rate-time analysis failing to correctly detect the onset of boundary-dominated flow (BDF). In contrast, Rapid RTA correctly detects the onset of BDF and presents a defined maximum value of the NPV vs. cluster spacing (number of fractures) function. This optimal value is a trade-off between the fracture treatment cost, the speed of recovery of hydrocarbons, and the value of money with time (discount rate). The major contribution of this work is the implementation of the Rapid RTA technique allowing fast computation of pressure-rate-time analysis and NPV calculations with computational times comparable with DCA for optimizing fracture cluster spacing and the total number of fractures of unconventional wells. We have developed a web-based application to give readers a hands-on experience of this new technique and to analyze and optimize the NPV of unconventional wells.

Prediction of Wells Performance and Producing Life Applying Production Decline Analysis

Analyzing the production data became a common method in modern reservoir engineering practice, which is used to dynamically indicate the reservoir's characteristics, predict long-term performance, and determine the reservoir's criteria through daily production analysis. Input data required to analyze production data is production rate and sometimes flow pressure (if any). The decline curve analysis method analyzes well production data to history matching and forecasts the future well performance. The decline curve analysis (DCA) method is a non-linear regression method for production history matching and prediction. In this study, we used the DCA method to analyze eight oil wells' production data produced from the Mishrif reservoir in an Iraqi oil field. This work dealt with the prediction of the reservoir’s performance and future production rate using the DCA method tool integrated with IPM- MBAL software. Production decline rate trend analysis was performed by generating standard curves utilizing exponential, hyperbolic, and harmonic decline model equations based on historical production data. Afterward, a production forecast model was built for production to 2025. The producing life for each well at the economic flow rate (300 bbl/Day) is estimated. Results from the production forecast showed that the exponential and hyperbolic decline models yielded 54.057 MMSTB and 80.73 MMSTB, respectively. The decline rates for hyperbolic and exponential models till 2025 were estimated as 4614.25 STB/Day and 1153.1STB/Day.

Application of machine learning to decline curve analysis (DCA) for gas-condensate production wells with complex production history due to add-on perforation of new reservoirs

For every oil and gas operator, DCA plays an essential role since it provides crucial information for production planning and reserves estimation. DCA is the analysis of the decline in production rate or pressure over time, which can be done by fitting a curve through production or pressure historical data points and making a forecast for the well based on the assumption that the same declining trend will continue in the future. However, the conventional DCA method has been shown to have some limitations. On the other hand, machine learning has been vigorously and extensively researched in the last decade; its applications can be found in every aspect of life as well as in the oil and gas industry. Therefore, it is the ideal time to study the application of machine learning to DCA, to complement this important analysis. In this case study, machine learning was used to predict the decline of wellhead pressure, thereby determining well life as well as estimating reserves. The method was applied to 2 wells with very complex production histories due to add-on perforation of new reservoirs. The prediction was verified to have high reliability by comparison with dynamic modeling results.

PRODUCTION FORECASTING USING ARPS DECLINE CURVE MODEL WITH THE EFFECT OF ARTIFICIAL LIFT INSTALLATION

There are many methods for predicting the production performance of oil wells, using the simplest method by looking at the declining trend of production, such as Decline Curve Analysis (DCA), Material Balanced, or using reservoir simulations. Each of these methods has its advantages and disadvantages. The DCA method, the Arps method, is often used in production forecast analysis to predict production performance and estimate remaining reserves. However, the limitation of this method is that if the production system changes, the trend of decline will also change. At the same time, the application in the field of taking the trend of decreasing production does not pay attention to changes in the production system. This study aims to see that changes in the well production system will affect the downward trend of well production, estimated ultimate recovery (EUR) value, and well lifetime. To see the effect of these changes, the initial data tested used the results of reservoir simulations and field data. From the evaluation results, it is found that if the production system changes during the production time, for example, from changing natural flow using artificial lifting assistance, the trend taken from the production profile will follow the behaviour of the reservoir if the trend is taken in the last system from the production profile, not from the start of production. If the downward trend is taken without regard to the changing system, then the prediction results will not be appropriate

Data-Driven Inversion-Free Workflow of Well Performance Forecast Under Uncertainty for Fractured Shale Gas Reservoirs

Abstract Well performance prediction and uncertainty quantification of fractured shale reservoir are crucial aspects of efficient development and economic management of unconventional oil and gas resources. The uncertainty related to the characterization of fracture topology is highly difficult to be quantified by the conventional model-based history matching procedure in practical applications. Data-space inversion (DSI) is a recently developed inversion-free and rapid forecast approach that directly samples the posterior distribution of quantities of interest using only prior model simulation results and historical data. This paper presents some comparative studies between a recent DSI implementation based on iterative ensemble smoother (DSI-IES), model-based history matching, and conventional decline curve analysis (DCA) for shale gas rate forecast. The DSI-IES method treats the shale gas production rate as target variables, which are directly predicted via conditioning to historical data. Dimensionality reduction is also used to regularize the time-series production data by low-order representation. This approach is tested on two examples with increasing complexity, e.g., a fractured vertical well and a multistage fractured horizontal well in the actual fractured Barnett shale reservoir. The results indicate that compared with the traditional history matching and DCA methods, the DSI-IES obtains high robustness with a high computational efficiency. The application of data-space inversion-free method can effectively tap the potential value directly from historical data, which provides theoretical guidance and technical support for rapid decision-making and risk assessment.

Gaussian Decline Curve Analysis of Hydraulically Fractured Wells in Shale Plays: Examples from HFTS-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

The present study shows how new Gaussian solutions of the pressure diffusion equation can be applied to model the pressure depletion of reservoirs produced with hydraulically multi-fractured well systems. Three practical application modes are discussed: (1) Gaussian decline curve analysis (DCA), (2) Gaussian pressure-transient analysis (PTA) and (3) Gaussian reservoir models (GRMs). The Gaussian DCA is a new history matching tool for production forecasting, which uses only one matching parameter and therefore is more practical than hyperbolic DCA methods. The Gaussian DCA was compared with the traditional Arps DCA through production analysis of 11 wells in the Wolfcamp Formation at Hydraulic Fracture Test Site-1 (HFTS-1). The hydraulic diffusivity of the reservoir region drained by the well system can be accurately estimated based on Gaussian DCA matches. Next, Gaussian PTA was used to infer the variation in effective fracture half-length of the hydraulic fractures in the HFTS-1 wells. Also included in this study is a brief example of how the full GRM solution can accurately track the fluid flow-paths in a reservoir and predict the consequent production rates of hydraulically fractured well systems. The GRM can model reservoir depletion and the associated well rates for single parent wells as well as for arrays of multiple parent–parent and parent–child wells.

Economic Limit Estimation and Production Forecasting in a Field by Using Decline Curve Analysis Method

In this paper, the decline curve analysis method is performed according to a new economic limit in a field named X (for confidential reasons). The production data are processed in EXCEL software and the forecasting is made in MBAL software by respecting the following steps: Firstly, identify the decline zone and determine the decline model on the production history, secondly estimate a new economic limit by using the actual oil price, thirdly, forecast the production and determine the reserves still recoverable and fourthly, make an economic evaluation. The interpretation of the result show that the decline zone is extended on 540 days and the decline curve showed an exponential decline by the next, the new economic limit is estimated at 381.86 Sm3/Day which allow concluding that, the life of the field X is extended from 367 days with 253,935.46 Sm3 as the ultimate recoverable reserves. By the end, these reserves can generate 11,581,676 USD as net income. The above results make it possible to understand the influence of the economic limit and by ricochet the oil price on the lifetime of field X and on its recoverable reserves. Depending on the company, the makeable net income can be set as profitable or nonprofitable. In the positive case this could allow the reopening of the field X.

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network, and their performance in the tight gas reservoir production prediction is investigated and compared. To further improve the performance of the sequence learning method, the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm, which can greatly simplify the optimization process of the neural network model in an automated manner. Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained. The predictive performance of LSTM and GRU is similar, and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

GAUSS-JACOBI’S ITERATION METHOD FOR NATURAL GAS WELLS PRODUCTION FORECAST USING DECLINE CURVE ANALYSIS

This study seeks to perform production forecasts of gas wells using the concept of decline curve analysis (DCA) and regression analysis. The regression analysis was performed by fitting a polynomial curve fitting model to a set of production history data, so as to determine the initial gas flowrate. In addition, Gauss Jacobi method was used to provide a solution to the solved five-by-five matrix in order to compute values for the regression coefficients. Subsequently, a computer program “ROTEX” was developed to predict the efficacy of the proposed DCA and regression analysis method. Three wells (WELL Y1, Y2 and Y3) from a field in the Niger Delta region were evaluated with respect to forecasting future gas rates till abandonment. Consequently, Well Y1 was observed to have the potential to continue producing for the next 30 years, while Well Y2 and Y3 have the potential to produce for roughly five years before they reach abandonment. R-squared values were computed for each case, as a means to validate the integrity of the fitted regression curves to the production history data. All R-squared values were observed to be very close to unity and are thus considered reliable.

Gaussian Decline Curve Analysis Method: A Fast Tool for Analyzing Hydraulically Fractured Wells in Shale Plays Demonstrated with Examples from Hfts-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

Gaussian Decline Curve Analysis Method: A Fast Tool for Analyzing Hydraulically Fractured Wells in Shale Plays Demonstrated with Examples from Hfts-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

Gaussian Decline Curve Analysis Method: A Fast Tool for Analyzing Hydraulically Fractured Wells in Shale Plays Demonstrated with Examples from Hfts-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

Gaussian Decline Curve Analysis Method: A Fast Tool for Analyzing Hydraulically Fractured Wells in Shale Plays Demonstrated with Examples from Hfts-1 (Hydraulic Fracture Test Site-1, Midland Basin, West Texas)

Conditions for Effective Application of the Decline Curve Analysis Method

Determining the reliable values of the filtration parameters of productive reservoirs is the most important task in monitoring the processes of reserve production. Hydrodynamic studies of wells by the pressure build-up method, as well as a modern method based on production curve analysis (Decline Curve Analysis (DCA)), are some of the effective methods for solving this problem. This paper is devoted to assessing the reliability of these two methods in determining the filtration parameters of terrigenous and carbonaceous productive deposits of oil fields in the Perm Krai. The materials of 150 conditioned and highly informative (obtained using high-precision depth instruments) studies of wells were used to solve this problem, including 100 studies conducted in terrigenous reservoirs (C1v) and 50 carried out in carbonate reservoirs (C2b). To solve the problem, an effective tool was used—multivariate regression analysis. This approach is new and has not been previously used to assess the reliability of determining the filtration parameters of reservoir systems by different research methods. With its use, a series of statistical models with varying degrees of detail was built. A series of multivariate mathematical models of well flow rates using the filtration parameters determined for each of the methods is constructed. The inclusion or non-inclusion of these filtration parameters in the resulting flow rate models allows us to give a reasonable assessment of the possibility of using the pressure build-up method and the DCA method. All the constructed models are characterized by high statistical estimates: in all cases, a high value of the determination coefficient was obtained, and the probability of an error in all cases was significantly less than 5%. As applied to the fields under consideration, it was found that both methods demonstrate stable results in terrigenous reservoirs. The permeability determined by the DCA method and the pressure build-up curve does not control the flow of the fluid in carbonate reservoirs, which proves the complexity of the filtration processes occurring in them. The DCA method is recommended for use to determine the permeability and skin factor in the conditions of terrigenous reservoirs.

Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations

A comprehensive overview and analysis of the productivity of 1216 recently abandoned multi-stage hydraulically fractured horizontal wells from five shale formations in the United States (US) is presented in this study. In this study, two decline curve analysis (DCA) methods were used to match actual production history data using least-squares fitting to find the best fit production parameters to reliably forecast production. The production history matching conducted resulted in very accurate matches (correlation coefficient of 0.99) between actual production data and the two DCA methods (Arps hyperbolic decline and stretched exponential production decline (SEPD) models). Using the outcomes from production history matching, universal averages of decline parameters for Arps hyperbolic decline and SEPD models were developed for each of the five formations. Furthermore, hindcasting was performed by matching a portion of the known production history and comparing the remaining portion of the known production history to the forecast. The Arps hyperbolic decline and SEPD methods were used to match production using only limited early production data (three months, six months, one year and two years). The main goals for fitting the DCA model to early production data was to estimate the optimum decline parameters that are then used to forecast production and estimate ultimate recovery. Production history matching using limited early production periods produced accurate production forecasts using as few as six months of production history (correlation coefficients between 0.85 and 0.94 using Arps hyperbolic decline). The main outcome of this study was a production analysis conducted on the production data of more than 1000 wells from five different shale formations to present the expected production behaviors of similar wells. Different production key performance indicators (KPIs) such as average well life, cumulative production volumes at different periods, average drop in production rate within the first year of production, average time to reach maximum flow rate, and the maximum flow rate were measured on all the wells from the five formations to provide an overview of the production performance of each formation.

A straight-line DCA for a gas reservoir

In decline curve analysis (DCA), the decline exponent (b) characterizes the variation of decline rate (D) with production, and significantly affects the long-term shape of the production profile. In most currently-available DCA methods, the decline exponent is assumed to be constant during the whole production period. However, production decline of a gas reservoir does not follow exponential (b = 0), hyperbolic (0 < b < 1), or harmonic (b = 1) decline profiles, and instead the value of b varies with production time. Application of current DCA methods to predict future performance yields erroneous results, especially for high-pressure gas reservoirs.The objective of this paper is to propose a new method in which the effect of depletion-related changes in the value of b is taken into account. The new methodology can be applied to analyze the production data from a gas well during the entire boundary dominated production period, allowing Gas Initially-In-Place (G) to be estimated at any stage of that period. The proposed methodology is validated using both synthetic and field examples.

A New Approach To Estimating Ultimate Recovery for Multistage Hydraulically Fractured Horizontal Wells by Utilizing Completion Parameters Using Machine Learning

Summary Reserves estimation is an essential part of developing any reservoir. Predicting the long-term production performance and estimated ultimate recovery (EUR) in unconventional wells has always been a challenge. Developing a reliable and accurate production forecast in the oil and gas industry is mandatory because it plays a crucial part in decision-making. Several methods are used to estimate EUR in the oil and gas industry, and each has its advantages and limitations. Decline curve analysis (DCA) is a traditional reserves estimation technique that is widely used to estimate EUR in conventional reservoirs. However, when it comes to unconventional reservoirs, traditional methods are frequently unreliable for predicting production trends for low-permeability plays. In recent years, many approaches have been developed to accommodate the high complexity of unconventional plays and establish reliable estimates of reserves. This paper provides a methodology to predict EUR for multistage hydraulically fractured horizontal wells that outperforms many current methods, incorporates completion data, and overcomes some of the limitations of using DCA or other traditional methods to forecast production. This new approach is introduced to predict EUR for multistage hydraulically fractured horizontal wells and is presented as a workflow consisting of production history matching and forecasting using DCA combined with artificial neural network (ANN) predictive models. The developed workflow combines production history data, forecasting using DCA models and completion data to enhance EUR predictions. The predictive models use ANN techniques to predict EUR given short early production history data (3 months to 2 years). The new approach was developed and tested using actual production and completion data from 989 multistage hydraulically fractured horizontal wells from four different formations. Sixteen models were developed (four models for each formation) varying in terms of input parameters, structure, and the production history data period it requires. The developed models showed high accuracy (correlation coefficients of 0.85 to 0.99) in predicting EUR given only 3 months to 2 years of production data. The developed models use production forecasts from different DCA models along with well completion data to improve EUR predictions. Using completion parameters in predicting EUR along with the typical DCA is a major addition provided by this study. The end product of this work is a comprehensive workflow to predict EUR that can be implemented in different formations by using well completion data along with early production history data.

Oil Field Development Scenario With Geological Characterization In "Alpha" Field Of South Sumatra Basin

The Structure of Alpha Field located in South Sumatra Basin, Geographically located around Musi Banyu Asin, Sub-district Sungai Lilin. The target reservoir to be developed is Baturaja Formation, domination with sandstone, and consisting of 9 layers. To forecast the production of the field, it use decline curve analysis. From the existing oil production data, exponential and hyperbole charts can be created. So, the analysis of decline curve method can forecast how much hydrocarbon can be produce from the reservoir. To get the maximal recovery of oil production, field development can be done. In making a geological model or geological characterization of the Alpha field using the Oasis Montaj software. Based on the calculation results, scenario 1 which is the basecase, is unable to produce for 20 years. The cumulative value of production is 76,243.8 with an RF of 9.67%. Scenario 2 is basecase/scenario 1 plus several workover wells (WO) capable of producing for 20 years with a cumulative production value of 484,748.7 STB with an Recovery Factor (RF) value of 10.47%. Whereas scenario 3 is scenario 2 plus several workover wells with the addition of infill wells capable of producing for 20 years with a cumulative production of 2,036,907.1 STB with an RF value of 13.50%. Of the three scenarios, the best scenario is scenario 3 because it has the highest cumulative value of production and RF and is certainly capable of producing for 20 years after a simulation using the DeclineCurve Analysis method. There is a relationship between geological characterization and field development scenarios where to provide information about Alpha Field, especially which parts and wells have high productivity so that it can be used as a reference in field development, especially when determining drill points for infill wells and well workovers.

AI-Based Decline-Curve Analysis Manages Reservoir Performance

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197142, “Artificial-Intelligence-Based, Automated Decline-Curve Analysis for Reservoir Performance Management: A Giant Sandstone Reservoir Case Study,” by Amir Kianinejad, Rami Kansao, and Agustin Maqui, Quantum Reservoir Impact, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Decline-curve analysis (DCA) is one of the more widely used forms of data analysis that evaluates well behavior and forecasts future well and field production and reserves. In the complete paper, the authors develop and deploy technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion. This method contrasts with manual DCA, the common practice of the industry. Introduction DCA is used commonly to estimate reservoir and well productivity and ultimate recovery and evaluate reserves. Such analyses are usually performed manually through a curve-fitting process by reservoir and production engineers using their best judgement and experience. Subjectivity is often a large component of such estimations, as are the experience and objectives of the evaluator. The authors provide an alternative by developing and leveraging an augmented artificial-intelligence (AI), data-driven approach. Geological and engineering features are considered as well as operational conditions to bring domain knowledge into the analysis, resulting in more-accurate and -reliable predictions. In addition, the method is ex tended to conduct DCA probabilistically using quantile regression techniques.

Pre-Drilling Production Forecasting of Parent and Child Wells Using a 2-Segment Decline Curve Analysis (DCA) Method Based on an Analytical Flow-Cell Model Scaled by a Single Type Well

This paper advances a practical tool for production forecasting, using a 2-segment Decline Curve Analysis (DCA) method, based on an analytical flow-cell model for multi-stage fractured shale wells. The flow-cell model uses a type well and can forecast the production rate and estimated ultimate recovery (EUR) of newly planned wells, accounting for changes in completion design (fracture spacing, height, half-length), total well length, and well spacing. The basic equations for the flow-cell model have been derived in two earlier papers, the first one dedicated to well forecasts with fracture down-spacing, the second one to well performance forecasts when inter-well spacing changes (and for wells drilled at different times, to account for parent-child well interaction). The present paper provides a practical workflow, introduces correction parameters to account for acreage quality and fracture treatment quality. Further adjustments to the flow-cell model based 2-segment DCA method are made after history matching field data and numerical reservoir simulations, which indicate that terminal decline is not exponential (b = 0) but hyperbolic (with 0 &lt; b&lt; 1). The timing for the onset of boundary dominated flow was also better constrained, using inputs from a reservoir simulator. The new 2-segment DCA method is applied to real field data from the Eagle Ford Formation. Among the major insights of our analyses are: (1) fracture down-spacing does not increase the long-term EUR, and (2) fracture down-spacing of real wells does not result in the rate increases predicted by either the flow-cell model based 2-segment DCA (or its matching reservoir simulations) with the assumed perfect fractures in the down-spaced well models. Our conclusion is that real wells with down-spaced fracture clusters, involving up to 5000 perforations, are unlikely to develop successful hydraulic fractures from each cluster. The fracture treatment quality factor (TQF) or failure rate (1-TQF) can be estimated by comparing the actual well performance with the well forecast based on the ideal well model (albeit flow-cell model or reservoir model, both history-matched on the type curve).

Haynesville Shale: Predicting Long-Term Production and Residual Analysis To Identify Well Interference and Fracture Hits

Summary In this study we analyze the production data from 2,755 horizontal wells in the Haynesville Shale. Correlations were generated to predict 4-, 5-, 6-, and 7-year cumulative production from initial, 6-, 12-, and 24-month production data. These correlations can help in field-development planning and economic analysis. The residuals (Predicted – Actual Cumulative Production) from these correlations were also analyzed, and this technique can be used to identify wells affected by interference, refracturing, fracture hits, and other factors. The cumulative-production estimates from the developed correlations were also compared with the corresponding estimates from the decline-curve-analysis (DCA) equations (seven different DCA methods were used). The accuracy of prediction made on the basis of correlations developed in this study is close to that for various standard DCA methods published and used in the industry. The developed correlations are faster to use and easier to implement for a large number of wells. Another of our objectives was to develop P10, P50, and P90 type curves for the Haynesville Shale using the available production data. A subset of the total wells (i.e., 150 wells evenly distributed throughout the study area) was used for predicting type curves. These type curves were generated and compared using different DCA models. The different DCA methods predicted an uncertainty of 5 to 27% for the P10, P50, and P90 production profiles.

Variable Exponential Decline: Modified Arps To Characterize Unconventional-Shale Production Performance

Summary Different decline-curve-analysis (DCA) methods have been proposed to predict the production performance of both conventional and unconventional-shale reservoirs. These methods range from empirical to semiempirical and theoretical. The different methods were developed using specific data sets and have their own assumptions and limitations, and thus are not universally applicable. This study shows that a DCA method should be capable of simultaneously modeling the flow regime prevalent around the well and the changes in reservoir properties with time, to be able to successfully represent the production performance of the well and predict future performance. In shales, flow regimes can be linear, bilinear, multifracture linear, post-linear, stimulated-reservoir-volume (SRV) -dominated boundary flow, or compound linear. The change in fracture conductivity caused by fines migration, embedment, crushing, diagenesis, and change in stresses because of production is another important phenomenon for which a DCA method must account. This study critically analyzes various proposed historical DCA methods with respect to their capability to model fracture-flow regimes and changes in fracture conductivity with time. Upon close examination, it was found that both the linear-flow regimes and changes in fracture conductivity with time follow a power-law function. Thus, the reason for the successful application of the Arps (1944) hyperbolic, power-law-exponential (PLE), stretched-exponential-decline (SEPD), and Duong (2010) methods is that decline rates in these methods are a power-law function or can be closely approximated by a power-law function. A new simplified decline-curve equation is proposed by modifying the existing Arps exponential-decline equation, where the constant-decline rate is replaced by a power-law-function variable decline rate. The application of this method is shown using production data from Haynesville Formation and Eagle Ford Formation shales. The average error in cumulative-production prediction for 20 wells was found to be only 3% in Haynesville wells and 2% in Eagle Ford wells. This method is very robust and can account for different flow regimes and changes in fracture conductivity with time.