Parameter determination for a numerical approach to undeveloped shale gas production estimation: The UK Bowland shale region application

Nwaobi, Ukadike; Anandarajah, Gabrial

doi:10.1016/j.jngse.2018.07.024

Cited by 19 publications

(4 citation statements)

References 23 publications

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“…Thus, due to this glacial pace progress and lack of exploratory drilling, a question among many others remains unanswered-whether it is economically viable to extract shale gas in current gas price environment for a company? also pointed by Nwaobi and Anandarajah [10].…”

Section: Introductionmentioning

confidence: 62%

“…Also, an initial production rate of 3666 Mcfd has been assumed by Acquah-Andoh [29] for Bowland shale play. Same is replicated here, however, to incorporate probability, normal distribution i.e., Mean = 3666, Std Dev = 250 ( Figure 3) is fitted in this work, which might be regarded as highly optimistic assumption if compared to results of Nwaobi and Anandarajah [10] which using a numerical model based on several geological parameters, estimate the production potential of Bowland shale as 14,700 scfd from eight wells developed and modelled conservatively. The equation reveals two most important parameters needed to define and estimate the production profile of a well over its lifetime: well's initial production rate and decline rate or its trajectory over the well's lifetime.…”

Section: Initial Production Ratementioning

confidence: 99%

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Economic Feasibility Analysis of Shale Gas Extraction from UK’s Carboniferous Bowland-Hodder Shale Unit

Ahmed

Gomari

2018

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For many years, shale gas exploitation has been generating contradictory views in the UK and remains subject of rising debates throughout these years. Favorably backed by the government, looking upon it as potential mechanism for gas import independency and competitiveness in global gas industry while strongly opposed by stakeholders (mainly public), idea of shale gas exploitation remains disputed with no substantial progress in past years. And this irresolution is worsened by obscurity of estimates for potential reserves and conflicting assessments on potential impact of shale gas. Yet, in case shale industry is signaled to search and extract resources, there remains another scrutiny stage that shale industry will be subjected to i.e., its extraction must be economically feasible as extracting unconventional resources is financially expensive and riskier than conventional. Hence, this study aims at analyzing the economics of UK's most prolific Bowland shale play development by a financial model that discovers gas prices range required to earn capital cost on investment in Bowland shale play and is tested on three development plans where it determines that based on given set of hypothesis and past decade's average gas price of $6.52/Mcf, none of the development plans hold enough probability of adding value, however, hybrid plan formulated by combination of consistent drilling and refracturing proves as economically sustainable with a RGP mean $7.21/Mcf, significantly lower than $9.76/Mcf mean for 'drilling only' plan. It is found that required gas price is most sensitive to initial production rate and drilling costs where ±10% variation offsets RGP by ≈±8% and ±7%.

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Section: Introductionmentioning

confidence: 62%

Section: Initial Production Ratementioning

confidence: 99%

Economic Feasibility Analysis of Shale Gas Extraction from UK’s Carboniferous Bowland-Hodder Shale Unit

Ahmed

Gomari

2018

Resources

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“…NRS method relies on a numerical model, which tends to achieve good performance and evaluate reservoir geological heterogeneity [5,6]. Furthermore, the NRS models have some limitations, including being time-consuming, cumbersome [7], and it requires constructing an accurate static model, history matching, and other dynamic model parameters. Furthermore, analytical techniques are employed to compute various forms of wellbore flow rate adjustments.…”

Section: Introductionmentioning

confidence: 99%

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

et al. 2021

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Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

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“…Reservoir numerical simulation is a common method for production rate forecasting because of its reliable physical background [4,5]. However, it is time-consuming and demands many reservoir features, such as rock mechanical parameters and fluid property parameters [6]. Inaccurate or missing parameters will lead to unbelievable production predictions.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao

et al. 2020

Energies

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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.

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Parameter determination for a numerical approach to undeveloped shale gas production estimation: The UK Bowland shale region application

Cited by 19 publications

References 23 publications

Economic Feasibility Analysis of Shale Gas Extraction from UK’s Carboniferous Bowland-Hodder Shale Unit

Economic Feasibility Analysis of Shale Gas Extraction from UK’s Carboniferous Bowland-Hodder Shale Unit

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

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

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