Decline Curve Analysis 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.

Prophet modeling for oil production forecasting in an enhanced oil recovery field

Accurate daily oil production forecasting is essential for efficient reservoir management and investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and conformance-dominated fields is a complex process due to the nonlinear, voluminous, and often uncertain nature of reservoir parameters and hidden factors. As a result, conventional tools such as decline curve analysis frequently fail to accurately predict daily oil production in conformance-controlled areas. In contrast, machine learning works efficiently for large datasets, even if the parameter values are unknown. The current study employs a Prophet time series forecasting method for five oil production wells in an EOR applied field, but it fails to achieve the desired sweep efficiency. This study compares the results of conventional decline curve analysis (DCA) and popular autoregressive integrated moving average time series forecasting methods with the Prophet model. This is the first attempt to use Prophet for oil well production forecasting, where polymer flooding is used. In all, 60% of the data are used for training, and the remaining 40% are used for testing. The Prophet shows the best performance for all the wells. This study is also the first to handle shut-in data using the Prophet model for oil production. Well-2 achieves the highest accuracy after incorporating shut-in results, with an R2 score of 92%. The result shows that though the DCA performs reasonably well with higher linearity and trend stationary data, Prophet modeling shows superior results than conventional DCA for all EOR applied producing wells.

Novel methodology to couple decline curve analysis with CFD reservoir simulations for complex shale gas reservoirs

AbstractShale reservoirs are highly complex and are difficult to study using conventional reservoir simulation tools. This study introduces a novel methodology for estimating production from complex shale gas reservoirs by coupling decline curve analysis (DCA) with computational fluid dynamics (CFD) simulations. The proposed method uses exponential DCA to analyze production data from a dual porosity–permeability shale gas transport model. These complexities include fracture characteristics, geomechanical properties, nanopore confinement effects, and multiple flow mechanisms contributing to the total production performance. The shale gas transport model is validated through historical production data from Marcellus shale. The new methodology also tests fracture characteristics. It shows that increased porosity and permeability will increase the recoverable reserves but will have varying effects on the decline rate. The paper demonstrates the advantages of the proposed methodology over conventional reservoir simulation tools. It provides insights into the factors affecting shale gas production performance through the inclusion of the complexities of an unconventional shale gas reservoir. The paper provides a proof of concept on the particular reservoir of which the field data is provided—Barnett and Marcellus Shale.

Benchmarking study of coal seam gas production from Brownfield to Greenfield wells in the Surat basin, Australia

Benchmarking coal seam gas (CSG) production from Brownfield to Greenfield is an important step for assessing the development plan for Greenfield. However, the observed Brownfield production data is impacted by many factors besides reservoir properties, e.g., well spacing, well efficiency factor (WEFAC), surface pipeline network. This paper presents a case study in the Surat Basin to discuss how to benchmark CSG production from Brownfield to Greenfield. The selected Brownfield study area includes 143 wells and the Greenfield 450 proposed wells. Various tools for static modelling, decline curve analysis, and dynamic modelling were used in this study. Results show that the peak gas rate decreases quickly and the time to peak gas rate increases quickly when the WEFAC is less than 0.65. The peak gas rate is reached when the cumulative water production is between 39% and 46% of water-in-place for the selected box model. After introducing the wells’ online time ratio or WEFAC from the daily production data and the ratio of the observed cumulative water production to the expected cumulative water production, gas production rates can be predicted based on the modified Morse potential energy equation. Artificial neural networks (ANN) can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties; while the ramp-up rate can be calculated from the time to peak gas rate.

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 for Productivity Prediction in Tight Gas Reservoirs

Accurate well productivity prediction plays a significant role in formulating reservoir development plans. However, traditional well productivity prediction methods lack accuracy in tight gas reservoirs; therefore, this paper quantitatively evaluates the correlations between absolute open flow and the critical parameters for Linxing tight gas reservoirs through statistical analysis. Dominant control factors are obtained by considering reservoir engineering theories, and a novel machine learning-based well productivity prediction method is proposed for tight gas reservoirs. The adaptability of the productivity prediction model is assessed through machine learning and field data analysis. Combined with the typical decline curve analysis, the estimated ultimate recovery (EUR) of a single well in the tight gas reservoir is forecasted in an appropriate range. The results of the study include 10 parameters (such as gas saturation) identified as the dominant controlling factors for well productivity and geological factors that impact the productivity in this area compared to fracturing parameters. According to the prediction results of the three models, the R2 of Support Vector Regression (SVR), Back Propagation (BP), and Random Forest (RF) models are 0.72, 0.87, and 0.91, respectively. The results indicate that RF has a more accurate prediction. In addition, the RF model is more suitable for medium and high-production wells based on the actual field data. Based on this model, it is verified that the productivity of low-producing wells is affected by water production. This study confirms the model’s reliability and application value by predicting recoverable reserves for a single well.

Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale

Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1160 (95% CI 1140–1180) metric tons (mt) per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 107 L/10-year) and southwest (median = 5.56 × 107 L/10-year) regions of the state, influencing lithium yield estimates of individual wells in southwest [2.90 (95% CI 2.80–2.99) mt/10-year] and northeast [1.96 (CI 1.86–2.07) mt/10-year] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate substantial lithium yields from Marcellus PW, though regional variability in chemistry and production may impact recovery efficiencies.

Hybrid Approach Proves Effective in Multiwell Forecasting in Unconventionals

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3855422, “RGNet for Multiwell Forecasting in Unconventional Reservoirs,” by Zhenyu Guo, SPE, and Sathish Sankaran, SPE, Xecta Digital Labs, and Ying Li, The University of Tulsa. The paper has not been peer reviewed. _ The aim of the complete paper is to propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment. The method the authors propose is the reservoir graph network (RGNet) model. By reducing system complexity while maintaining fundamental physics, the model provides an efficient and accurate way to model, history-match, and predict unconventional wells. Compared with a full-physics model that takes from hours to days to run, the described model only takes from seconds to minutes. Performance Analysis of Unconventional Reservoirs Developing unconventional reservoirs is a complex process involving well targeting, timing, spacing, and completion design for horizontal wells with hydraulic fractures. Among commonly used methods, decline curve analysis (DCA) can execute quick well-performance analysis without considering the complex physics in unconventional reservoirs piece by piece. The convenient aspect of DCA is that practitioners can obtain forecast results quickly by providing historical production data. However, because DCA has some strong assumptions on operational conditions and does not incorporate the key physics of the flow, it may fail to predict accurately in many situations. As a simple, fast tool for analyzing the capacity of a reservoir, rate transient analysis (RTA) has been used to model and forecast unconventional wells. Both pressure and rate data are considered to generate solutions that are more credible than those generated by DCA, where only rate information is used. The drawback of RTA is related to significant assumptions used to derive analytical solutions (e.g., homogeneous reservoir and simple planar fractures with uniform properties). Moreover, it is not an easy task to acquire some of the information required by RTA. Full-physics reservoir simulation models are used as the most-rigorous approach to understand dynamics of unconventional reservoirs because they allow for different complexities for modeling unconventionals. However, because it takes tremendous effort for information gathering, geological/fracture modeling, and history matching, applying this approach to large-scale reservoirs is not tractable. Based on the idea of diffusive time of flight (DTOF), the RGNet model was developed for reservoir modeling, history matching, forecasting, and optimization. It can model multiple wells with communications. In this work, the authors incorporate pressure dependency of pore volumes and transmissibility into RGNet while modeling unconventional reservoirs. Methodology The basic idea of the described model comes from the DTOF transformation that converts the 3D diffusivity problem into a 1D problem. The complete paper provides several equations that enable this conversion.

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Increasing oil production from a reservoir can be achieved by decreasing the distance between the injector and the producer through a process known as infill drilling, which involves a pattern water flood. The main objective of this study is to provide a comprehensive overview of the optimal infill well location and the research and applications available to enhance the oil recovery factor, leading to increased economic profits. one effective empirical approach used in this study is based on decline curve analysis, which analyzes the production history of the well to determine the final economic recovery. Additionally, a numerical method that combines numerical simulation and optimization techniques has been proven to be successful in determining optimal infill drilling locations. The research results show that the volumetric computation of oil in place is a useful method for estimating the number of infill wells needed, but it does not consider heterogeneity and continuity. On the other hand, the numerical simulation and optimization techniques can quantify the remaining mobile oil post-infill drilling and establish optimal pattern configurations for maximum recovery at their centers.

Bayesian-based probabilistic decline curve analysis study in unconventional reservoirs

Bayesian-based probabilistic decline curve analysis study in unconventional reservoirs

Analysis of risk factors affecting the prognosis of patients with sepsis and construction of nomogram prediction model.

This study analyzed the clinical data of 200 sepsis patients, exploring the risk factors that affect patient prognosis and providing the basis for clinically targeted intervention to improve patient prognosis. 200 septic patients were admitted to Yulin Second Hospital, and they were divided into a survival group of 151 patients and a death group of 49 patients, according to their clinical outcomes on admission. The relevant clinical parameters within 24 h of admission were collected, and the independent risk factors affecting the prognosis of septic patients were analyzed by multivariate Logistic regression. R language 4.21 software was used to construct a nomogram prediction model. The receiver operating characteristic curve was used to evaluate the discrimination of the nomogram model, and decline curve analysis was drawn to evaluate the effectiveness of the model. In the nomogram prediction model, age, the Acute Physiology and Chronic Health Scoring System Domain (APACHE II) score, the Sequential Organ Failure Assessment (SOFA) score, C-reactive protein (CRP), total bilirubin, albumin (Alb), urea nitrogen, creatinine, and lactate (Lac) were independent risk factors for death in septic patients. The area under the receiver operating characteristic (ROC) curve for predicting the prognosis of septic patients was 0.597-1.000, and the calibration curve tends to be the ideal curve. The model had good discrimination and calibration and had high accuracy in evaluating septic patients. The modeling curves in the decline curve analysis (DCA) were all above the two extreme curves, which had good clinical value. Nine clinical variables have been found to be independent risk factors for death in septic patients. The prediction model established based on this has good accuracy, discrimination, and consistency in predicting the prognosis of sepsis patients.

An initial production assessment of oil and gas in a specific field was carried out using nodal analysis and decline curve analysis

Global petroleum consumption is on the rise, and conventional production from reservoirs is struggling to meet this increasing demand. As a result, it becomes imperative to exploit non-traditional resources. Unconventional resources are often characterized by their exceptionally low permeability. To tap into these resources, horizontal wells are drilled, and multiple hydraulic fractures are employed to establish reservoirs. Achieving economically viable production rates necessitates the efficient flow of hydrocarbons through these hydraulic fractures toward the wellbore. The analysis of production decline involves the examination of historical trends in well and reservoir performance, focusing on plots that display the relationship between production rate and time as well as cumulative production. In the petroleum industry, four techniques are commonly used to assess reserves: decline curve analysis, numerical simulation, material balance, and volumetric analysis. Decline curve analysis, utilizing the least squares method, is particularly effective in fitting a best-fit equation to a set of data points. By calculating the future rate at any desired time and determining reserves from a specific point to an economically viable period, this approach proves valuable for estimating critical parameters such as the initial decline rate (D), initial rate (qi), and the hyperbolic exponent (b). These values can then be graphed over time to illustrate the decline in production rates.

Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis

We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”, that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common.

Skin Factor Consideration in Decline Curve Analysis

Summary This paper introduces an approach for the impact of skin factor on the decline curve analysis of hydraulically fractured reservoirs. The objective is to consider this impact in the production forecasting and the ultimate recovery estimation. The approach focuses on reducing the uncertainties that could be raised from this impact on the production history and increasing the accuracy of the predicted flow rates. It proposes an easy and promising tool for the decline curve analysis that could be applied confidently to different oil- and gas-producing wells and different reservoirs. This approach utilizes the rate-normalized flow rate derivative β behavior of the fractured reservoirs. This derivative demonstrates a constant behavior with time for each flow regime when the production history has not undergone the impact of the skin factor. However, the constant behavior no longer exists when this impact has influenced the production history. Instead, a power-law type model governs the relationship between the flow rate derivative and production time. New analytical flow rate decline curve models, exponential-type, are derived from the flow rate derivative power-law type models for the flow regimes. Different models for calculating the skin factor are developed for the three linear flow regimes that could be observed during the transient state flow conditions. The proposed flow rate models are used to simulate the production history and forecast the future performance. Moreover, the hydraulic fracture conductivity can be calculated using these models as well as the flow rate loss caused by skin factor. Several case studies are examined by the proposed approach where the production history is used to characterize the dominant flow regimes. The study has reached several observations and conclusions. The impact of skin factor is seen clearly throughout transient state flow regimes; however, this impact declines sharply before reaching pseudosteady-state flow (boundary-dominated flow regime). The impact of the skin factor alternates the constant behavior of the flow rate derivative with time to a power-law type relationship. A straight line of a slope (0.5) is diagnosed during hydraulic fracture and formation linear flow regime on the log-log plot of the flow rate derivative β and time, while the bilinear flow regime demonstrates a straight line of a slope (0.25). Because of the skin factor, exponential decline curve models replace the power-law type models of the flow rate during the abovementioned flow regimes. These models exhibit an excellent match between the calculated flow rate and the production history. The maximum flow rate loss occurs during very early production time even though the skin factor during this time is less than the intermediate production time. This study presents a novel approach for the decline curve analysis taking into account the impact of skin factor. The novelty is represented by considering the flow regimes in the production forecasting of hydraulically fractured reservoirs. This approach is smoothly applied to predict the future performance with no need to know the wellbore and reservoir parameters. It can be used to predict the declining flow rate for a constant or varied bottomhole flowing pressure.

Advancing Reservoir Performance Optimization through UserFriendly Excel VBA Software Development

This study addresses the pressing demand for streamlined field performance analysis within oil and natural gas development, which currently necessitates substantial expertise and time investment. The principal aim involves developing a user-friendly software tool dedicated to optimizing reservoir rendition. Leveraging the Havlena and Odeh material balance straight line equation form, this tool integrates a zero-dimensional reservoir model with Decline Curve Analysis. The implementation of this user-friendly software enables achievable material balance optimization by aligning cumulative produced fluid with historical production data, akin to the widely acknowledged concept of history matching in material balance analysis. This accomplishment not only facilitates further endeavors like pressure simulation and forecasting but also augments the comprehension of reservoir dynamics. The analysis incorporated three datasets: one modeled from L.P. Dake's textbook and two drawn from real-life reservoirs in the Niger Delta. Assessment of estimated water influx and cumulative oil production indicated minimal discrepancies between Np Real and Np model for these reservoirs. Consequently, material balance history matching for these reservoirs seems feasible. Achieving reservoir rendition optimization involved a Microsoft Excel VBA code consisting of two hundred and thirty-five (235) lines, meticulously designed to replicate MBAL functionality. The software demonstrated congruent outcomes with MBAL, affirming its reliability for history matching and enhancing reservoir performance. We strongly advocate the utilization of this software for optimizing reservoir performance across diverse global regions. Its capacity to streamline field analysis could significantly benefit the oil and natural gas industry.

Bayesian variable pressure decline-curve analysis for shale gas wells

Decline-curve analysis and production forecasting are usually performed from a deterministic standpoint (point estimation). This approach does not quantify the uncertainty of the model's parameters and thus, the model's estimated ultimate recovery. In addition, decline-curve models do not consider the variations in the bottomhole flowing pressure, which can greatly impact the accuracy of the model's predictions. This work combines a new technique that incorporates variable bottomhole flowing pressure conditions into decline-curve models with Bayesian inference to improve the accuracy of production history-matches while quantifying the uncertainty of the model's parameters and its future production prediction. The method provides fast production history-matches and forecasts of shale gas wells (taking around 1 min per well) and it is more accurate than traditional decline-curve analysis for wells subject to variable bottomhole flowing pressure conditions while quantifying the uncertainty in the model's parameters and estimated ultimate recovery. The main contribution of this work is the illustration of a new method for probabilistic variable pressure decline-curve analysis. We present the application of this workflow for shale gas wells.

Well Production Decline Models Considering Mechanical Skin Factor

Production decline analysis is an essential tool in predicting reservoir performance and in estimating reservoir properties. In its most basic form, decline curve analysis is to a large extent based on Arps’ empirical models that have little theoretical basis. But the mechanical skin factor due to formation damage is not taken into account in the production decline models available in the literature. The conventional models presented in the literature are mostly empirical and not grounded in fundamental in theory. New theoretical models are developed in this paper to forecast the production decline of a well producing in a closed circular or rectangular reservoir at constant bottomhole pressure, and mechanical skin factor due to formation damage is taken into account. Both the transient flow behavior and boundary‐dominated flow behavior are incorporated; the effects of reservoir size, well location, and mechanical skin factor on transient flow rate and pressure drop in the reservoir are studied. Analytical solutions are obtained through combinations of Green’s function and inverse Laplace transform. Stehfest’s algorithm is used to convert the obtained solution from the Laplace transform domain into the real time domain. The proposed models in this paper are based on analytical solutions to diffusivity equations in porous media, and the solutions are fast and practical tools to forecast the production performance of a well producing at constant flowing bottomhole pressure.

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.

Blasingame production decline curve analysis for fractured tight sand gas wells based on embedded discrete fracture model

Blasingame production decline curve analysis (PDCA) is crucial for extracting natural gas from fractured tight sandstone reservoirs (FTSRs). The natural gas flow in FTSRs is complex due to high-velocity non-Darcy (hvnD) flow, threshold pressure gradient (TPG), and stress sensitivity of reservoirs. These factors pose a challenge in solving nonlinear equations. In this paper, we establish a mathematical model for the flow of natural gas in FTSRs. The fracture is characterized by a hvnD flow coupled using an embedded discrete fracture model (EDFM). The gas flow in the matrix is coupled with the TPG. Stress sensitivity has been considered in both fractures and matrix. The finite volume method (FVM) was employed to discretize and solve the nonlinear equations in the model. The pressure-normalized rate algorithm and gas flow model were used to present a Blasingame production decline curve for fractured horizontal wells in FTSRs. The Blasingame production decline curve can be used to identify natural gas flow regimes, matrix petrophysical properties, and fracture parameters. The effect of the hvnD flow in fractures, the lvnD flow in the matrix (TPG), stress sensitivity, fracture parameters, and production schedule on the Blasingame production decline curve and pressure profile are discussed in detail. This study provides scientific and theoretical support for predicting gas wells performance and the gas production decline law in FTSRs.