Decline Curve Analysis Research Articles

Time Series Analysis of Production Decline in Carbonate Reservoirs with Machine Learning

Classical decline methods, such as Arps yield decline curve analysis, have advantages of simple principles and convenient applications, and they are widely used for yield decline analysis. However, for carbonate reservoirs with high initial production, rapid decline, and large production fluctuations, with most wells having no stable production period, the adaptability of traditional decline methods is inadequate. Hence, there is an urgent need to develop a new decline analysis method. Although machine learning methods based on multiple regression and deep learning have been applied to unconventional oil reservoirs in recent years, their application effects have been unsatisfactory. For example, prediction errors based on multiple regression machine learning methods are relatively large, and deep learning sample requirements and the actual conditions of reservoir management do not match. In this study, a new equal probability gene expression programming (EP-GEP) method was developed to overcome the shortcomings of the conventional Arps decline model in the production decline analysis of carbonate reservoirs. Through model validation and comparative analysis of prediction effects, it was proven that the EP-GEP model exhibited good prediction accuracy, and the average relative error was significantly smaller than those of the traditional Arps model and existing machine learning methods. The successful application of the proposed method in the production decline analysis of carbonate reservoirs is expected to provide a new decline analysis tool for field reservoir engineers.

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.

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.

Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Production prediction plays an important role in decision making, development planning, and economic evaluation during the exploration and development period. However, applying traditional methods for production forecasting of newly developed wells in the conglomerate reservoir is restricted by limited historical data, complex fracture propagation, and frequent operational changes. This study proposed a Gated Recurrent Unit (GRU) neural network-based model to achieve batch production forecasting in M conglomerate reservoir of China, which tackles the limitations of traditional decline curve analysis and conventional time-series prediction methods. The model is trained by four features of production rate, tubing pressure (TP), choke size (CS), and shut-in period (SI) from 70 multistage hydraulic fractured horizontal wells. Firstly, a comprehensive data preprocessing is implemented, including excluding unfit wells, data screening, feature selection, partitioning data set, z-score normalization, and format conversion. Then, the four-feature model is compared with the model considering production only, and it is found that with frequent oilfield operations changes, the four-feature model could accurately capture the complex variance pattern of production rate. Further, Random Forest (RF) is employed to optimize the prediction results of GRU. For a fair evaluation, the performance of the proposed model is compared with that of simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural network. The results show that the proposed approach outperforms the others in prediction accuracy and generalization ability. It is worth mentioning that under the guidance of continuous learning, the GRU model can be updated as soon as more wells become available.

Optimal field development and production design for unconventional reservoirs: A case study from Central Sub-Basin, Permian Basin, new Mexico

The planning and developmental decisions succeeding the discovery of an unconventional oil and gas field is of utmost importance in attaining successful exploitation and production from these tight reservoirs. The development and production of hydrocarbon from these unconventional reservoirs require requires adequate planning and execution which could be challenging, due to the sequence of heterogeneous lithologies of these formations. In this study, we designed and presented a comprehensive field development and production program that is representative of the unconventional oil and gas development in the Central sub-basin of the Permian Basin. To design and achieve optimal field development and production for unconventional reservoirs in the Central sub-basin of the Permian Basin, New Mexico, we utilized field data from an oil and gas well that was drilled to a TVD/MD of 2290 m with a PBTD of 2270 m. Subsequently, we conducted the petrophysical evaluation of the logs and utilized a decline curve analysis for reservoir evaluation of all the members of the formation. We also designed and developed comprehensive drilling, completions, and facilities programs. Lastly, we incorporated an economic analysis of our optimized field development and production design. Our results provide a valuable plan and critical decision-making insight for optimizing field development and production of unconventional reservoirs in the Permian Basin and across the world.

Decline Curve Analysis of Shale Oil Production Using a Constrained Monte Carlo Technique

Decline Curve Analysis of Shale Oil Production Using a Constrained Monte Carlo Technique

Machine-Learning Approach Determines Spatial Variation in Shale Decline Curves

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196110, “Machine Learning of Spatially Varying Decline Curves for the Duvernay Formation,” by Aleksandr Bakay, Jef Caers, and Tapan Mukerji, SPE, Stanford University, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. The two most common techniques for forecasting production performance for a new shale well are decline- (type) curve analysis and machine learning. The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters. The methodology allows the user to decide whether the focus should be purely on forecast quality or on a combination of forecast and clustering quality. The resulting model will enable the prediction and uncertainty quantification of production profiles for new target wells or areas in the basin. Methods of Forecasting Production Performance Decline-curve analysis is the most-popular technique for forecasting production performance in shale formations because of the need for fast decisions. The technique involves borrowing decline curves from the closest wells or from wells with similar geological, completion, or fluid properties. The idea of decline-curve analysis is based on the fact that a similar production profile is expected from the closest wells or from wells with similar properties. However, the process is often manual and very subjective. As a result of the approach, each existing well is assigned to a particular cluster of decline curves, each cluster having a certain typical decline curve. Clusters can be spatial or represented in completion variable space. To obtain a production forecast for a new well, the authors use the decline curve from a cluster to which it is believed that the new well will belong, usually sampling from a cluster map. The second approach to forecasting shale production performance is machine learning that focuses on statistical correlations. A statistical model is created that connects decline curves with the same well parameters used in decline curve analysis. Typically, the result is a trained machine-learning model. For a new well, it provides production performance, with or without an uncertainty range. Additionally, maps can be created of forecasted production profiles or total recovered fluid. The objective of the project described in the complete paper was to provide a methodology that creates clusters of decline curves and estimates decline curves for a particular location or set of variables. The intent was to limit the manual aspect of clustering and create a robust work flow.

Shale gas production forecasting is an ill-posed inverse problem and requires regularization

Abstract Decline curve analysis (DCA)—the extrapolation of a production curve model fitted to a well’s past production—remains the standard approach for forecasting unconventional oil and gas production. A scaling curve based on a fractured shale gas reservoir model was recently proposed as a way of connecting this approach with underlying physics but as this paper shows, it actually generates worse predictions than the traditional non-physical modified Arps curve. DCA is fundamentally an ill-posed inverse problem with the defining characteristic of model sloppiness, or parameter correlation. Today’s unconventional resource forecasts can be substantially improved by using information from offset wells to reduce ill-posedness through Tikhonov regularization. This versatile approach nearly matches a deep neural network approach introduced here, which has practical limitations but offers a model-neutral benchmark of achievable extrapolation accuracy. There is a natural connection between regularization and a Bayesian formulation which is also highlighted. This paper evaluates long-term forecasting accuracy for these techniques using historic production data from 4457 Barnett shale wells, and reveals that the overlooked step of regularization is more critical than choice of model.

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.

Empirical methods of decline-curve analysis for shale gas reservoirs: Review, evaluation, and application

Shale gas reservoirs are accessed using horizontal wells with fracturing technology. To improve production, the bottom-hole pressure is usually controlled. However, predicting the estimated ultimate recovery of the well becomes difficult with this production mode because the decline curves have a long tail and the production data changes dramatically. Comparing six decline-curve analysis methods shows that the empirical method is easiest. Therefore, 24 empirical models are summarized, and 18 basic models are further analyzed by using actual production data. The results show that most empirical models are based on exponential or power functions, especially using the exponential function widely in recent years. Forecasting production in these models does not always appear as a monotonic decline with the influence of model structures. Some models can transform into others under certain conditions. The dimensions of some models are not uniform because the units of their time exponents are always neglected. Empirical models are influenced by data, and none of the models can be applied in every case because of varying data. The number of reliable models increases with an increase in the amount of data and a decrease in fluctuation. The relationship between empirical models and data characteristics rather than flow regimes should be noticed. General methods of noise reduction are not effective with the drastic fluctuations of shale gas wells, so improving production data preprocessing is important. Although model recommendations are given in this paper, a detailed selection principle should be further studied based on more data from different shale gas reservoirs.

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

Normalized Cumulative Production Curves Estimate Ultimate Recovery

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199981, “Use of Normalized Cumulative Production Curves To Estimate Ultimate Recovery of Unconventional Plays in North America,” by Ivan Olea, Hamed Tabatabaie, SPE, and Louis Mattar, SPE, IHS Markit, et al., prepared for the 2020 SPE Canada Unconventional Resources Conference, Calgary, 15-16 September. The paper has not been peer reviewed. Operators and investors are interested in finding better metrics to evaluate the production performance of unconventional multifractured horizontal wells (MFHWs). The complete paper discusses the use of cumulative production ratio curves normalized to a given reference volume in time for different unconventional plays in North America to investigate the median trend for each play and the median ultimate recovery per play. The paper discusses the choice of 12-month cumulative production for a reference volume as a normalization parameter. Introduction Many methods exist for forecasting the production rate from unconventional reservoirs, but all have limitations. Recently, several publications have appeared relating the expected ultimate recovery (EUR) to the initial rate or the cumulative production after 3, 6, or 24 months. In the complete paper, these publications are reviewed, and their learnings extended, to several unconventional reservoirs. Work in 2018 studied 147 MFHWs covering many formations in the Permian Basin and a wide range of input variables and determined EUR using rate transient analysis, numerical simulation, and decline-curve analysis. The authors of that work compared the EUR with various cumulative production intervals (3, 6, 12, and 24 months) and concluded that the correlation with 3 months was poor; 24 months’ cumulative production was an accurate predictor of EUR but was not considered to be an early-enough predictor. That work’s authors chose 12 months as a preferred early-time predictor of EUR and justified its use by stating that operating conditions have usually stabilized by that time. A universal type curve of cumulative production was created as a percent of EUR vs. years of production (e.g., after 1 year of production, 33% of the EUR has been produced). This type curve accounted for different well lengths and completions and both strong and weak wells. The universality of the type curve is consistent with the understanding that the factors that make a well a high- or low-rate producer affect the 12-month cumulative and the EUR proportionally. A different work from 2018 studied approximately 3,000 wells in the Delaware Basin to determine an early indicator of long-term performance. These authors used not only the EUR but also the actual cumulative production from 252 wells with 6 years of production history. Results showed that the correlation coefficient between the 6-year production and various early-time indicators (cumulative production at 30, 60, 90, and 180 days and 1, 2, 3, and 5 years) improves from 0.23 (for 30 days) to 0.73 (for 1 year) and only minimally thereafter. A 2012 work studied public production data from more than 6,000 wells from the Barnett, Fayetteville, and Woodford shales. These authors binned the data into quartiles and used a key performance indicator (KPI) that combines a confusion matrix and a cost matrix (penalty function). Results indicated that the prediction ability of various metrics ranged from 48% (3-month cumulative production) to 85% (2-year cumulative production) and that, in some cases, the peak month production KPI was only marginally lower than the 6- or 12-month cumulative.

Applying Reservoir-Engineering Methods to Well-Placement Optimization Algorithms for Improved Performance

Summary Optimization is an essential task for field development planning because it has the potential to increase the economic value of the project. Because of advancements in technology, optimization is now shifting from manual to automated schemes. However, because of the complexity and high dimensionality of the problem of field-development optimization, the automated scheme receives less attention. In this paper, we demonstrate an increased efficiency of optimization algorithms in well-placement problems by application of reservoir-engineering practices. Analytical and empirical solutions to the flow equations as developed for reservoir engineering are added to the algorithm to improve the sampling efficiency of the algorithm. The main engineering methods that have been applied in this project are Buckley-Leverett (BL) and decline curve analysis (DCA). We begin by modifying and validating these analytical functions for different reservoir and well conditions. They are then applied in two stages: the initialization stage and optimization stage. In the former stage, numerous samples are created and evaluated by these analytical functions to identify good candidates for the initial population of the optimization algorithm. In the latter stage, these functions are used as a proxy model of the full simulation. The proposed algorithm is compared with a base-case approach in which no engineering-based analytical functions are used. The use of these engineering aspects is shown to be useful in both stages. The results also show that considerable computation time can be saved by applying these engineering aspects.

A novel workflow for water flowback RTA analysis to rank the shale quality and estimate fracture geometry

Recently, flowback data analysis enabled us to evaluate important fracture parameters including fracture conductivity and volume in unconventional reservoirs. To perform the analysis, diagnostic plots, straight-line techniques, and history-matching techniques have been used. Immediate water and gas production usually occurs on flowback in shale gas wells. In this paper, a novel workflow is developed for the analysis of water flowback data and the early-time production of shale gas wells. This analysis then helps to define the movable water and the applicability of the soaking process on these wells.Rate transient analysis (RTA) combined with decline curve analysis (DCA) were used to analyze different shale gas wells. Effective fracture volume and geometry were calculated from the RTA analysis. The estimated ultimate water recovery (EURw) was calculated from DCA. The calculated original water-in-place (OWIP) and the EURw were compared to the injected fracturing fluid. The impact of the soaking process on the well performance was studied.Results showed that in high-quality shale wells, with no movable formation water, gas kicked off early. OWIP was equal to the total injected water volume, and boundary dominated flow (BDF) regime was observed with no transient period. The performance of these wells improved with soaking.On the other hand, in low-quality shale wells, initial formation water saturation was higher than the connate water, and water production was from both the frac fluid and formation water. Consequently, gas kicked off later and transient flow regimes were observed. Transient flow regimes were used to estimate the fracture stimulated area. In this case, however, soaking had a negative impact on the performance as the movable water saturation was high.Honoring the flowback data can be used to estimate the fracture geometry and judge the quality of the shale formation and its validity for the soaking process.

Impact of Geological Variables in Controlling Oil-Reservoir Performance: An Insight from a Machine-Learning Technique

Summary Predicting oilfield performance is extremely challenging because of the large number of variables that can influence and control it. Traditional methods such as decline-curve analysis have been commonly used but have been shown to have significant shortcomings. In recent years, advances in machine learning (ML) have provided a new suite of tools to tackle complex multivariant problems such as understanding oil-reservoir performance and predicating the final recovery factor. In this study, the application of a random-forest algorithm to train three predictive models and investigate the influence of the various input variables was investigated. To train the algorithm, a database was built that includes information on 32 variables from 93 reservoirs from the Norwegian Continental Shelf. These variables control or potentially influence field performance and include factors that are a function of geology, subsurface conditions, fluids, and the engineering decisions taken in field development. In addition to these controlling parameters, data were also recorded for the fields that record performance. These included information on the estimated recovery factor and production rates. Eighty percent of the data were input into the random-forest algorithm to train the models, whereas 20% were retained to blind test the subsequent models. Model accuracy was measured by comparing actual and predicted observations for each prediction metric using an R2 score, mean square error, and root mean square error. The production-rate model had a mean square error of 0.004, whereas the mean square error for recovery factor was 0.024. Estimates of average monthly depletion rate have a mean square error of 0.0104. Predictor importance estimates indicate that geology/depth-dependent variables such as stratigraphic heterogeneity, reservoir depth of burial, average porosity, and diagenetic impact are among the variables with high importance in predicting recovery factor. When predicting reservoir-oil rate, the most important variables are related to field size, such as cumulative oil produced, number of wells, oil in place (OIP), and bulk rock volume. In this study, we provide data-driven insight into understanding the relationship between subsurface and engineering conditions of reservoir producibility; we also provide a tool for predicating reservoir performance within a basin or region.

Physical Scaling of Oil Production Rates and Ultimate Recovery from All Horizontal Wells in the Bakken Shale

A recent study by the Wall Street Journal reveals that the hydrofractured horizontal wells in shales have been producing less than the industrial forecasts with the empirical hyperbolic decline curve analysis (DCA). As an alternative to DCA, we introduce a simple, fast and accurate method of estimating ultimate recovery in oil shales. We adopt a physics-based scaling approach to analyze oil rates and ultimate recovery from 14,888 active horizontal oil wells in the Bakken shale. To predict the Estimated Ultimate Recovery (EUR), we collapse production records from individual horizontal shale oil wells onto two segments of a master curve: (1) We find that cumulative oil production from 4845 wells is still growing linearly with the square root of time; and (2) 6401 wells are already in exponential decline after approximately seven years on production. In addition, 2363 wells have discontinuous production records, because of refracturing or changes in downhole flowing pressure, and are matched with a linear combination of scaling curves superposed in time. The remaining 1279 new wells with less than 12 months on production have too few production records to allow for robust matches. These wells are scaled with the slopes of other comparable wells in the square-root-of-time flow regime. In the end, we predict that total ultimate recovery from all existing horizontal wells in Bakken will be some 4.5 billion barrels of oil. We also find that wells completed in the Middle Bakken formation, in general, produce more oil than those completed in the Upper Three Forks formation. The newly completed longer wells with larger hydrofractures have higher initial production rates, but they decline faster and have EURs similar to the cheaper old wells. There is little correlation among EUR, lateral length, and the number and size of hydrofractures. Therefore, technology may not help much in boosting production of new wells completed in the poor immature areas along the edges of the Williston Basin. Operators and policymakers may use our findings to optimize the possible futures of the Bakken shale and other plays. More importantly, the petroleum industry may adopt our physics-based method as an alternative to the overly optimistic hyperbolic DCA that yields an ‘illusory picture’ of shale oil resources.

Application of Decline Curve Analysis for Estimating Different Properties of Closed Fractured Reservoirs for Vertical Wells

In this paper, decline curve analysis is used for estimating different parameters of bounded naturally fractured reservoirs. This analysis technique is based on rate transient technique, and it is shown that if production rate is plotted against time on a semi-log graph, straight lines are obtained that can be used to determine important parameters of the closed fractured reservoirs. The equations are based on Warren and Root model. The comparison between the results of this technique and those of the conventional methods confirms its high proficiency in transient well testing. It should be noted that in conventional decline curve methods, parameters such as interporosity flow parameter and storage capacity ratio must be first obtained by previous methods like the build-up analysis, but in the proposed method all the main reservoir parameters can be calculated directly, which is one of the advantages of this method. This paper focuses on the interpretation of rate tests, and the starting points and slopes of straight lines are utilized with proper equations to solve directly for various properties. The main important aspect of the presented method is its accuracy since analytical solutions are used for calculating reservoir parameters.

Using Different Methods to Predict Oil in Place in Mishrif Formation / Amara Oil Field

The reserve estimation process is continuous during the life of the field due to risk and inaccuracy that are considered an endemic problem thereby must be studied. Furthermore, the truth and properly defined hydrocarbon content can be identified just only at the field depletion. As a result, reserve estimation challenge is a function of time and available data. Reserve estimation can be divided into five types: analogy, volumetric, decline curve analysis, material balance and reservoir simulation, each of them differs from another to the kind of data required. The choice of the suitable and appropriate method relies on reservoir maturity, heterogeneity in the reservoir and data acquisition required. In this research, three types of reserve estimation used for the Mishrif formation / Amara oil field volumetric approach in mathematic formula (deterministic side) and Monte Carlo Simulation technique (probabilistic side), material balance equation identified by MBAL software and reservoir simulation adopted by Petrel software geological model. The results from these three methods were applied by the volumetric method in the deterministic side equal to (2.25 MMMSTB) and probabilistic side equal to (1.24, 2.22, 3.55) MMMSTB P90, P50, P10 respectively. OOIP was determined by MBAL software equal to (2.82 MMMSTB). Finally, the volume calculation of OOIP by using the petrel static model was (1.92 MMMSTB). The percentage error between material balance and the volumetric equation was equal to 20% while the percentage error between the volumetric method and petrel software was 17%.