Decline Curve Analysis Models Research Articles

Long-term production forecast in tight and shale reservoirs: Adapting probability density functions for decline curve analysis

Decline curve analysis (DCA) is a popular method for predicting the production of oil and gas wells over time. It involves using historical data to match a mathematical function with the production data, which can then be used to forecast future production. However, DCA in unconventional tight and shale reservoirs poses unique challenges. Unlike conventional reservoirs, these unconventional reservoirs exhibit a production decline that is both faster and more erratic. This behavior, combined with the inherent complexities of the reservoir properties and fluid flow mechanisms, makes it challenging for traditional DCA models to provide accurate predictions of future production.This study proposes a new approach for forecasting long-term production in tight and shale reservoirs, using probability density functions (PDFs) that have been adapted and evaluated for this purpose. The PDF-based DCA models were tested against traditional DCA models using data from over 80 wells in different unconventional basins across North America.Results show that the PDF-based models outperformed the traditional models, with the Dagum and Beta prime models achieving the highest performance scores on various statistical measures, especially in wells with limited production history for model training. To ensure accuracy, appropriate statistical tests should be used to validate the PDF-based decline curve analysis, such as calculating the mean absolute error (MAE) or other relevant metrics.Our research introduces a novel method of adapting PDFs for decline curve analysis, specifically tailored for tight and shale reservoirs. This adaptation offers a more accurate and comprehensive prediction of future production by effectively capturing the inherent uncertainty and variability seen in unconventional reservoirs. One of the standout features of our proposed PDF-based approach is its versatility. Unlike traditional models that may struggle with erratic data, our models can seamlessly fit production data, whether it possesses peaks or not. This approach could be valuable for producers and investors who want to make informed decisions about drilling and production in unconventional reservoirs.

Uncertainty quantification in the Permian Basin using conventional and modified bootstrap methodology

Various uncertainty quantification methodologies are presented using a combination of several deterministic decline curve analysis models and two bootstrapping algorithms. These probabilistic models are applied to 126 sample wells from the Permian basin. Results are presented for 12–72 months of production hindcast given an average well production history of 103 months. Based on the coverage rate and the forecast error (with the coverage rate being more significant in our choice of the best probabilistic models) and using up to one-half of the available production history for a group of sample wells from the Permian Basin, we find that the CBM-SEPD combination is the best probabilistic model for the Central Basin Platform, the MBM-Arps combination is the best probabilistic model for the Delaware Basin, the CBM-Arps is the best probabilistic model for the Midland Basin, and the best probabilistic model for the overall Permian Basin is the CBM-Arps when early time data is used as hindcast and CBM-SEPD for when one-quarter to one-half of the data is used as hindcast. When three-quarters or more of the available production history is used for analysis, the MBM-SEPD probabilistic model is the best combination in terms of both coverage rate and forecast error for all the sub-basins in the Permian. The novelty of this work lies in its extension of bootstrapping methods to other decline curve analysis models. This work also offers the engineer guidance on the best choice of probabilistic model whilst attempting to forecast production from the Permian Basin.

Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Probabilistic Decline Curve Analysis: State-of-the-Art Review

The decline curve analysis (DCA) technique is the simplest, fastest, least computationally demanding, and least data-required reservoir forecasting method. Assuming that the decline rate of the initial production data will continue in the future, the estimated ultimate recovery (EUR) can be determined at the end of the well/reservoir lifetime based on the declining mode. Many empirical DCA models have been developed to match different types of reservoirs as the decline rate varies from one well/reservoir to another. In addition to the uncertainties related to each DCA model’s performance, structure, and reliability, any of them can be used to estimate one deterministic value of the EUR, which, therefore, might be misleading with a bias of over- and/or under-estimation. To reduce the uncertainties related to the DCA, the EUR could be assumed to be within a certain range, with different levels of confidence. Probabilistic decline curve analysis (pDCA) is the method used to generate these confidence intervals (CIs), and many pDCA approaches have been introduced to reduce the uncertainties that come with the deterministic DCA. The selected probabilistic type of analysis (i.e., frequentist or Bayesian), the used DCA model(s), the type and the number of wells, the sampling technique of the data or the model’s parameters, and the parameters themselves undergo a probability distribution, and these are the main differences among all of these approaches and the factors that determine how each approach can quantify the uncertainties and mitigate them. In this work, the Bayesian and frequentist approaches are deeply discussed. In addition, the uncertainties of DCA are briefly discussed. Further, the bases of the different probabilistic analyses are explained. After that, 15 pDCA approaches are reviewed and summarized, and the differences among them are stated. The study concludes that Bayesian analysis is generally more effective than frequentist analysis, though with narrower CIs. However, the choice of DCA model and sampling algorithm can also affect the bounds of the CIs and the calculation of the EUR. Moreover, the pDCA approach is recommended for quantifying uncertainties in DCA, with narrower CIs that indicate greater effectiveness. However, the computational time and the number of iterations in sampling are also considered critical factors. That is why various assumptions and modifications have been made in the pDCA approaches, including the assumption of a certain probability distribution for the sampled parameters to improve their reliability of reserve estimation. The motivation behind this research was to present a full state-of-the-art review of the pDCA and the latest developments in this area of research.

Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis

Shale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically considered the easiest method for production prediction of unconventional reservoirs as it only requires production history. Besides uncertainties in selecting a suitable DCA model to match the production behavior of the shale gas wells, the production data are usually noisy because of the changing choke size used to control the bottom hole flowing pressure and the multiple shut-ins to remove the associated water. Removing this noise from the data is important for effective DCA prediction. In this study, 12 machine learning outlier detection algorithms were investigated to determine the one most suitable for improving the quality of production data. Five of them were found not suitable, as they remove complete portions of the production data rather than scattered data points. The other seven algorithms were deeply investigated, assuming that 20% of the production data are outliers. During the work, eight DCA models were studied and applied. Different recommendations were stated regarding their sensitivity to noise. The results showed that the clustered based outlier factor, k-nearest neighbor, and the angular based outlier factor algorithms are the most effective algorithms for improving the data quality for DCA, while the stochastic outlier selection and subspace outlier detection algorithms were found to be the least effective. Additionally, DCA models, such as the Arps, Duong, and Wang models, were found to be less sensitive to removing noise, even with different algorithms. Meanwhile, power law exponential, logistic growth model, and stretched exponent production decline models showed more sensitivity to removing the noise, with varying performance under different outlier-removal algorithms. This work introduces the best combination of DCA models and outlier-detection algorithms, which could be used to reduce the uncertainties related to production forecasting and reserve estimation of shale gas reservoirs.

On the Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs

Summary Several authors have worked on combining decline curve analysis (DCA) models and stochastic algorithms for probabilistic DCAs. However, there are no publications on the application of these probabilistic decline curve models to all the major shale basins in the United States. Also, several empirical and analytical decline curve models have been developed to fit historical production data better; there is no systematic investigation of the relevance of the efforts on new model development compared with the efforts to quantify the uncertainty associated with the “noise” in the historical data. This work compares the uncertainty associated with determining the best-fit model (epistemic uncertainty) with the uncertainty associated with the historical data (aleatoric uncertainty) and presents a procedure to find DCA-stochastic algorithm combinations that encompass the epistemic uncertainty. We investigated two Bayesian methods—the approximate Bayesian computation and the Gibbs sampler—and two frequentist methods—the conventional bootstrap (BS) and modified BS (MBS). These stochastic algorithms were combined with five empirical DCA models (Arps, Duong, power law, logistic growth, and stretched exponential decline) and the analytical Jacobi theta-2 model. We analyzed historical production data from 1,800 wells (300 wells from each of the six major shale basins studied) with historical data lengths ranging from 12 to 60 months. We show the errors associated with the assumption of a uniform distribution for the model parameters and present an approach for integrating informative prior (IP) probabilistic distributions instead of the noninformative prior (NIP) or uniform prior distributions. Our results indicate the superior performance of the Bayesian methods, especially at short hindcasts (12–24 months of production history). We observed that the duration of the historical production data was the most critical factor. Using long hindcasts (up to 60 months) leveled the performance of all probabilistic methods regardless of the decline curve model or statistical methodology used. Additionally, we showed that it is possible to find DCA-stochastic model combinations that reflect the epistemic uncertainty in most of the shale basins investigated. The novelty of this work lies in the development of IPs for the Bayesian methodologies and the development of a systematic approach to determine the combination of statistical methods and DCA models that encompasses the epistemic uncertainty. The proposed approach was implemented using open-source software packages to make our results reproducible and to facilitate its practical application in forecasting production in unconventional oil and gas reservoirs.

Removing the Outlier from the Production Data for the Decline Curve Analysis of Shale Gas Reservoirs: A Comparative Study Using Machine Learning.

Decline curve analysis (DCA) is one of the most common tools to estimate hydrocarbon reserves. Recently, many decline curve models have been developed for unconventional reservoirs because of the complex driving mechanisms and production systems of such resources. DCA is subjected to some uncertainties. These uncertainties are mainly related to the data size available for regression, the quality of the data, and the selected decline curve model/s to be used. In this research, first, 20 decline curve models were summarized. For each model, the four basic equations were completed analytically. Second, 16 decline curve models were used with different data sizes and then a machine learning (ML) algorithm was used to detect the outlier from shale gas production data with different thresholds of 10, 15, and 20%. After that, the 16 models were compared based on different data sizes and the three levels of data quality. The results showed differences among all models’ performances in the goodness of fitting and prediction reliability based on the data size. Also, some models are more sensitive to removing the outlier than others. For example, Duong and Wang’s models seemed to be less affected by removing the outlier compared to Weng, Hesieh, stretched exponential production decline (SEPD), logistic growth (LGM), and fractional decline curve (FDC) models. Further, the extended exponential decline curve analysis (EEDCA) and the hyperbolic–exponential hybrid decline (HEHD) models tended to underestimate the reserves, and by removing the outlier, they tended to be more underestimators. This work presented a comparative analysis among 16 different DCA models based on removing the outlier using ML. This may motivate researchers for further investigations to conclude which combination of the outlier removers and DCA models could be used to improve production forecasting and reserve estimation.

Logarithm Model for Decline Curve Analysis

A decline curve analysis model has been developed for flow rate proportional to the logarithm of time. The logarithm of time model requires careful interpretation of initial time dependence of volumetric flow rate. Applications of the model to shale gas production and shale oil production are presented.

Development of Decline Curve Analysis Parameters for Tight Oil Wells Using a Machine Learning Algorithm

To obtain a reliable production forecast, one has to establish a geological model with well logs and seismic data. The geological model usually has to be upscaled using certain upscaling techniques. Then, a dynamic reservoir model is constructed with another dataset, including completion data, production data, fluid properties, and relative permeability curves. At last, the dynamic model needs to be validated by a history matching process. This approach is data-intensive, time-consuming, and often not rigorously accomplished due to the lack of skillset and time. In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. Overall coefficients of determination ( R 2 ) of the three Arps decline curve factors were 0.966, 0.990, and 0.945. The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. As a result of the ML-DCA regression model, a complete understanding can be established of the impact of reservoir properties on the DCA model. The proposed ML-DCA model not only provides a quick and robust method for petroleum engineers to estimate production performance for unconventional reservoirs from reservoir and completion properties without full-field geocellular modeling but also can be used to optimize the completion and operation parameters for wells of interest.

Bayesian Deep Decline Curve Analysis: A New Approach for Well Oil Production Modeling and Forecasting

Summary Following the rapid growth of unconventional resources, many models and methods have been proposed for forecasting the performances of unconventional wells. Several studies have attempted to use machine learning (ML) for improving the forecasting. However, owing to limitations of ML in regard to long-term forecasts (e.g., the occurrence of unphysical results), most of these ML forecasts are not satisfactory. In this work, we propose, demonstrate, and discuss a new ML approach able to rapidly provide probabilistic, long-term forecasts of oil production rates from individual wells in a decline curve analysis (DCA) manner. The novelties of the proposed approach are as follows: (1) it combines an automated ML (AutoML) method for supervised learning and a Bayesian neural ordinary differential equation (BN-ODE) framework for time-series modeling; (2) it uses the DCA model to inform the BN-ODE framework of “physics” and regulate the BN-ODE forecasts; and (3) several completion parameters (such as locations, lengths, and slickwater volume) of individual wells are analyzed and included as the inputs of model building, in addition to measured oil production rate data. Specifically, AutoML method is first used to model the relationship between the well location, completion parameters, and the DCAs parameters, and the BN-ODE framework is then used to model the relationship between the DCAs parameters and the time-series oil production rates. A publicly accessible data set, consisting of completion parameters and oil production rates, of 396 horizontal wells in the Bakken Shale Formation is used to train and test the model of the proposed approach. The results lead to the conclusion that the proposed approach is practical for providing probabilistic, long-term forecasts of oil production from individual wells, given data of existing wells in the reservoir.

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.

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 novel decline curve regression procedure for analyzing shale gas production

Accurate prediction of gas production is critical for the shale gas development. Compared with the complicated physics-based simulation, the decline curve analysis (DCA) models based on parameter linearization or direct curve regression are much more straightforward and efficient. However, the linearization method might be time consuming in obtaining the parameters, and the direct regression might lead to large errors in production forecast as it assigns equal weight to the historical data. In this paper, we develop a new DCA procedure combining a data transformation method which convert the production data into logarithmic space, and a nonlinear regression algorithm. The new procedure can effectively capture the trend of late-time production history. The efficiency of the new procedure is verified with the production data of 550 gas wells in the Barnett and Marcellus shales. Based on these field data, we also analyze the performance of seven popular DCA models, i.e. Arps, power law exponential (PLE) decline, stretched exponential production decline (SEPD), Duong, Wang , variable decline modified Arps (VDMA) and logistic growth models. The performances of these models are compared and the tuned parameters for each model are provided. It is shown that the accuracy of the production forecast by the above models can be significantly improved by our new regression method, which is beneficial to the prediction and optimization of shale gas production.

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.

Probabilistic Decline Curve Analysis in the Permian Basin using Bayesian and Approximate Bayesian Inference

Summary In this work, a probabilistic methodology for decline curve analysis (DCA) in unconventional reservoirs is presented using several Bayesian model-fitting algorithms and deterministic models. The deterministic models considered are the power law exponential (PLE) model, the stretched exponential production decline (SEPD) model, Duong’s model, and the logistic growth analysis (LGA) model. Accurate production forecasting and uncertainty quantification were the primary objectives of this study. The Bayesian inferencing techniques described in this work utilize three sampling vehicles, namely the Gibbs sampling (implemented in OpenBUGS, an open-source software), the Metropolis-Hastings (MH) algorithm, and approximate Bayesian computation (ABC) to sample parameter values from their posterior distributions. These different sampling algorithms are applied in conjunction with DCA models to estimate DCA parameter prediction intervals. Using these prediction levels, production is forecasted, and uncertainty bounds are established. To examine its reliability, the methodology was tested on over 74 oil and gas wells located in the three main subplays of the Permian Basin, namely, the Delaware play, the Central Basin Platform, and the Midland play. Results show that the examined DCA-Bayesian models are well calibrated, result in low production errors, and narrow uncertainty bounds for the production history data sets. Also, the LGA model is best in terms of prediction errors for all algorithms except MH. The Gibbs algorithm is nearly the best algorithm in terms of prediction error for all DCA models except the Arps model. Prediction errors are the highest in the Central Basin Platform. The methodology was also successfully applied to unconventional reservoirs with as low as six months of available production history. Depending on the amount of production history data available, the probabilistic model that provides the best fit can vary. It is therefore recommended that all possible combinations of the deterministic and Bayesian model-fitting algorithms be applied to the available production history. This is to obtain more confidence in the conclusions related to the production forecasts, reserves estimate, and uncertainty bounds. The novelty of this methodology relies on using multiple combinations of DCA-Bayesian models to achieve accurate reserves estimates and narrow uncertainty bounds. This paper can help assess shale plays because some of the shale plays are in the early stages of development when productivity estimations are carried out.

Bayesian probabilistic dual-flow-regime decline curve analysis for complex production profile evaluation

Decline curve analysis (DCA) has been broadly applied in the oil and gas industry to predict future production and estimate reserves. The Arps decline model is one of the most popular DCA models used by the oil and gas industry. This model was designed for boundary dominated flow in conventional reservoirs. In practice, it has also been widely applied to both transient flow and boundary dominated flow in conventional reservoirs. Applied to a complex production profile involving dual flow regime from artificially enhanced tight/shale reservoir, each production segment needs to be analyzed separately. Although several analytical decline curve models have been developed in recent years to address the problem, none of them is flexible enough to model both production profiles from a single flow regime and a dual flow regime.In this study, we introduce a new production decline analysis model that can fit both production profiles from single flow regime and dual flow regime without the need to separate the production segments. We also introduce a probabilistic measure using Bayesian Markov Chain Monte Carlo simulation to determine the existence of dual flow regimes, the location of the regime switch, and uncertainties in parameter estimations with the new model. The proposed Probabilistic Dual Regime Decline Curve Analysis (PDR-DCA) method is tested on 344 horizontal Northern Montney gas wells.This proposed Bayesian approach provides a workflow to generate probabilistic decline curve for both single and dual segment production profile, probabilistic type well curve for a well group, and production prediction for well with very short production history.

Decline curve analysis for multiple-fractured horizontal wells in tight oil reservoirs

The production of multiple-fractured horizontal wells (MFHW) in tight oil reservoirs decreases rapidly in the initial period. It then enters a stable state of the low production with a slow loss-ratio. Because of its complicated production dynamic characteristics, the conventional rate-decline analysis method is no longer applicable. We adopt the semi- analytical solution of the MFHW five-region linear flow model to get the multiple-fractured dimensionless rate-decline analysis curves and continuously calculate the loss-ratio of the decline curves. Based on the characteristics of the loss-ratio in the log-log plot, the decline curve analysis (DCA) model for tight oil MFHW is established. According to the DCA model, dimensionless production decline curves are obtained, and the sensitivity of the parameters in the model is analyzed. Finally, the DCA model is used to analyze the production rate decline of tight oil MFHW, and good matching results and production forecasts are achieved. This method provides a scientific basis for the rate-decline analysis of tight oil MFHW. Cited as : Xu, G., Yin., H., Yuan, H., Xing, C. Decline curve analysis for multiple-fractured horizontal wells in tight oil reservoirs. Advances in Geo-Energy Research, 2020, 4(3): 296-304, doi: 10.46690/ager.2020.03.07

New rate-decline forecast approach for low-permeability gas reservoirs with hydraulic fracturing treatments

Estimating production in low-permeability gas reservoirs accurately has been of more concern to oil and gas industries in recent years. Until now, various techniques to estimate well production have been developed. Among them, decline curve analysis models have been recognized as the most efficient and easiest approach to be applied. Unfortunately, each decline curve model has its own limitation and will not allow us to forecast production in low-permeability gas reservoirs with confidence. In this paper, firstly, the review of Arps, stretched exponential production decline (SEPD), and Duong methods is conducted explicitly to illustrate the reasons resulted in their limitations. Subsequently, a new empirical decline model with two fitting parameters is developed based on the proposed linear relation of logarithmic production versus the product of square root of time and logarithmic time, which takes the change of decline exponent with production time into account. In addition, a detailed procedure applying this novel decline model for low-permeability gas wells is presented, which is reliable and easy to be utilized. Furthermore, this new model is validated by numerically simulated cases and field observations, which appears good match between forecast rates calculated by this proposed method and numerically simulated rates/field rates. The comparison between this novel method and several traditional methods are further conducted, which indicates that this new approach leads to more reliable forecast than commonly utilized approaches. This work should provide a theoretical guidance to assist analysts in estimating hydrocarbon production rapidly and efficiently in low-permeability reservoirs.

Selection of decline curve analysis model using Akaike information criterion for unconventional reservoirs

Petroleum production from unconventional resources (specifically, tight and shale reservoirs) has become a significant portion of the supply in North America which led operating companies to look for more accurate methods for reserve evaluation and production forecasting. Typically, the operators resort to Decline Curve Analysis (DCA) to determine the Estimated Ultimate Recovery (EUR) of hydrocarbon reserves in both conventional and unconventional reservoirs. In shale wells, the best DCA model fitted to the transient flow regimes followed by Boundary Dominated Flow (BDF). Therefore, in this study, we propose a novel framework for systematic DCA model selection using a quantitative measure of goodness of production data fit from shale wells. For this purpose, we selected seven different, most frequently applied DCA models to evaluate large number of wells from the Montney Formation. To collect production data from the Montney wells, we used the GeoSCOUT package. Then, we fed these data into the DCA model selection workflow powered by the Nelder-Mead simplex algorithm and implemented using a computer programming language. The quantitative measure that helps identify the best fit of the data and the model is derived from the Information Theory (IT) and is known as Akaike Information Criterion (AIC). The obtained results show that our DCA model selection framework can identify the most appropriate DCA model for a given production history. Moreover, it gives a specific quantitative measure (AIC) of the fit that can be used for model ranking. The results show that more accurate values of b are necessary in Arps’ DCA models to match production data from the Montney shale wells, and identify the Logistic Growth Analysis Model (LGM) is the best fit for the majority of the selected wells followed by Extended Exponential Decline Model (EED), Duong, and Power Law Exponential Model (PLE).

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.