Production Forecasting in Shale Volatile Oil Reservoirs Using Reservoir Simulation, Empirical and Analytical Methods

Makinde, Ibukun; Lee, W. John

doi:10.15530/urtec-2016-2429922

Cited by 6 publications

(4 citation statements)

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“…Several ways to forecast oil/gas production or estimate reserves include reservoir simulation, analytical, and empirical methods. Reservoir simulation methods are the most reasonable and precise method (Makinde and Lee, 2016) due to modeling the complex physics of flow found in reservoirs.…”

Section: List Of Figures Pagementioning

confidence: 99%

See 1 more Smart Citation

On the Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs

Egbe

Awoleke

Olorode

et al. 2022

SPE Reservoir Evaluation &Amp; Engineering

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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.

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Section: List Of Figures Pagementioning

confidence: 99%

“…In addition, the author proved that a combination of SEPD and Arps gave a more accurate forecast of production than using single models alone. Makinde and Lee (2016) used diagnostic plots to determine when to apply hybrid models. They used log log plots of rate vs. time and rate vs. material balance time.…”

Section: List Of Figures Pagementioning

confidence: 99%

On the Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs

Egbe

Awoleke

Olorode

et al. 2022

SPE Reservoir Evaluation &Amp; Engineering

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“…These limitations highlight the need for more efficient and scalable forecasting methods. 5 Xu and Yu (2024) 6 emphasized the laborious and time-intensive process of building and tuning numerical simulations, while Makinde and Lee (2016) 7 pointed out the challenges posed by high computational costs and integration of real-time data. Hence, there is a pressing need to employ intelligent computing methods, such as big data and machine learning, to develop production forecasting approaches that consider liquid lifting measures.…”

Section: Introductionmentioning

confidence: 99%

Well Production Forecast Post-Liquid Lifting Measures: a Transformer-Based Seq2Seq Method with Attention Mechanism

Lu,

Cui,

Qian

et al. 2024

Energy Fuels

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Oilfield production forecasting plays a crucial role in petroleum production. However, traditional methods, such as the Arps decline curve, merely reflect the trend of production history and fail to comprehensively consider the impact of measurement adjustments on production. In contrast, numerical simulation methods, although capable of comprehensively considering various factors, face practical application limitations due to their complexity and high computational costs. This study proposes a method for oil well production forecasting that integrates the attention mechanism with the Seq2Seq structure. Leveraging data from multiple wells in the ultrahigh water cut phase of the SL oilfield in China, a learning sample data set is established using dynamic time warping (DTW) for feature sorting. A production forecasting model, a transformer-based Seq2Seq deep learning algorithm capable of considering the impact of liquid lifting measures, is established. Comprehensive testing validates the superior performance of the model, with coefficients of determination on the training, validation, and testing sets being 0.9730, 0.9649, and 0.9461, respectively, clearly outperforming the traditional long−short-term memory (LSTM) model. Taking nine sample wells as an example, the model predicts oil production for the next 12 months under different liquid lifting magnitudes and determines the optimal liquid lifting magnitudes and corresponding monthly average incremental oil levels with the aid of a genetic algorithm. Experimental results align well with the empirical trends of the target oilfield, demonstrating that this model can be used as an effective tool for predicting well production and conducting analysis of the potential of liquid lifting. By optimizing the liquid lifting magnitudes of oil wells through accurate oil production predictions, the model enhances intelligent decision-making and optimizes production strategies, providing significant practical value in actual oilfield management.

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“…Bhattacharya and Nikolaou [12] used PCA to analyze production history from unconventional gas reservoirs but did not forecast future production. Makinde and Lee [13] used the Principal Components Methodology (PCM) to forecast production from shale volatile oil reservoirs and compared the results to compositionally simulated data and production estimates from different decline curve analysis (DCA) models.…”

Section: Introductionmentioning

confidence: 99%

Statistical, Data-Driven Approach to Forecasting Production from Liquid-Rich Shale Reservoirs

Makinde¹

2017

OALib

Self Cite

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The oil and gas industry needs fast and simple techniques of forecasting oil and gas production. Forecasting production from unconventional, low permeability reservoirs is particularly challenging. As a contribution to the continuing efforts of finding solutions to this problem, this paper studies the use of a statistical, data-driven method of forecasting production from liquid-rich shale (LRS) reservoirs called the Principal Components Methodology (PCM). In this study, production of five different highly volatile and near-critical oil wells was simulated for 30 years with the aid of a commercial compositional simulator. Principal Components Methodology (PCM) was applied to production data from the representative wells by using Singular Value Decomposition (SVD) to calculate the principal components (PCs). These principal components were then used to forecast oil and solution gas production from the near-critical oil wells with production histories ranging from 0.5 to 2 years, and the results were compared to simulated data and the Modified Arps' decline model forecasts. Application of the PCM to field data is also included in this work. Various factors ranging from ultra-low permeability to multi-phase flow effects have plagued the mission of forecasting production from liquid rich shale reservoirs. Traditional decline curve analysis (DCA) methods have not been completely adequate for estimating production from shale reservoirs. The PCM method enables us to obtain the production decline structure that best captures the variance in the data from the representative wells considered. This technique eliminates the need for parameters like the hyperbolic decline exponents (b values) and the task of switching from one DCA model to another. Also, production forecasting can be done without necessarily using diagnostic plots. With PCM, production could be forecasted from liquid-rich shale reservoirs with reasonable certainty. This study presents an innovative and simple method of forecasting production from liquid-rich

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Production Forecasting in Shale Volatile Oil Reservoirs Using Reservoir Simulation, Empirical and Analytical Methods

Cited by 6 publications

References 0 publications

On the Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs

On the Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs

Well Production Forecast Post-Liquid Lifting Measures: a Transformer-Based Seq2Seq Method with Attention Mechanism

Statistical, Data-Driven Approach to Forecasting Production from Liquid-Rich Shale Reservoirs

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