Fast-Track Completion Decision Through Ensemble-Based Machine Learning

Han, Xue; Malpani, Raj; Agrawal, Shivam; Bukovac, Tomislav; Mahesh, Arathi L.; Judd, Tobias Conrad

doi:10.2118/196702-ms

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Optimization of production enhancement EC (evolutionary algorithm) [20] 2020; ML-PSO [21] 2024 ensemble learning [22] 2019; ANN [23] 2019…”

Section: Optimization Of Formation Stimulationmentioning

confidence: 99%

“…The reservoir in the Kuche foreland of the Tarim Basin in China is an ultra-deep HTHP (high-temperature, high-pressure) naturally fractured sandstone reservoir. Han Xue et al [22] applied ensemble learning to enhance the machine learning model, and they optimized the scheme by varying the well spacing, the number of fracturing stages, the number of clusters per stage, and the concentration of proppant. Huifeng Liu et al [24] applied a machine learning methodology to identify the main controlling factors, then used multiple regression modeling methods to correlate production enhancement parameters such as the fracturing fluid volume, injection rate, and proppant volume of the well with the incremental open flow after production enhancement and utilized machine learning to obtain the weights of these production enhancement parameters to optimize the well stimulation design.…”

Section: Design Optimizationmentioning

confidence: 99%

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Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues.

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“…Optimization of production enhancement EC (evolutionary algorithm) [20] 2020; ML-PSO [21] 2024 ensemble learning [22] 2019; ANN [23] 2019…”

Section: Optimization Of Formation Stimulationmentioning

confidence: 99%

Section: Design Optimizationmentioning

confidence: 99%

Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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“…AdaBoost adapts to the complexity of the data by giving more weight to instances that are challenging to predict, allowing it to excel in situations where the relationship between features and target values is non-linear or intricate. Xue et al [99] developed tree-based ensemble regression models (Polynomial regression, DTs, RFs, AdaBoost, and XGB) to optimize a completion development with the ultimate goal of improving the cumulative oil recovery for hydraulic-fractured reservoirs. First, the authors used the LH sampling method to randomly generate parameter samples.…”

Section: Static Machine Learning Modelsmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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“…The tree-based ensemble method was used as a proxy model of a numerical simulator of 2D discrete fracture networks. This proxy model can predict 5-year oil cumulative production (Xue et al, 2019) [3]. The regular neural network (NN), random forests (RFs), adaptive boosting (AdaBoost), and support vector machines (SVMs) were used to build several proxy models of numerical simulation codes which can predict the production and the net present value (NPV) of a horizontal well with parallel and unequal-length hydraulic fractures (Li et al, 2022) [4].…”

Section: Review Of Researchmentioning

confidence: 99%

“…However, the input variables of the proxy models mentioned in these studies are often oversimplified. For example, among the previously mentioned proxy models, CNN, GPR, and SVM (Wang et al, 2021) [1] require that the length of the hydraulically fractured fractures in the dataset take only a fixed number of four values; the training set of the MLP network (Wang et al, 2021) [2] contains only four variables, and these four features can only take a fixed number of four values; the tree-based ensemble model (Xue et al, 2019) [3] requires that the dataset can only have these four variables, which can only take a fixed number of three values; and the input data of the transformer (Wang et al [5]) need to be preprocessed by PCA (principal component analysis) to decrease its dimension, but the variables generated by PCA often pose challenges in terms of interpretation. Although reducing the dimensions of the input data or simplifying the input data can reduce the complexity of machine learning models and make training easier, this also decreases the performance of the proxy models.…”

Section: Review Of Researchmentioning

confidence: 99%

A Physics-Informed Neural Network Approach for Surrogating a Numerical Simulation of Fractured Horizontal Well Production Prediction

Jin,

Xia,

Jiang

2023

Energies

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With the popularity of deep learning (DL), more and more studies are focusing on replacing time-consuming numerical simulations with efficient surrogate models to predict the production of multi-stage fractured horizontal wells. Previous studies on constructing surrogate models for the prediction of the production of fractured horizontal wells often relied on directly applying existing deep learning architectures without incorporating physical constraints into the model. When dealing with the large number of variables necessary for characterizing the properties of fractures, the input variables of proxy models are often oversimplified; meanwhile, lots of physical information is lost. Consequently, predictions are sometimes physically inconsistent with the underlying principles of the domain. In this study, by modifying the traditional Seq2Seq (LSTM–LSTM) deep learning architecture, a physics-informed encoder–decoder (PIED) architecture was developed to surrogate the numerical simulation codes for predicting the production of horizontal wells with unequal-length intersecting hydraulic fractures on a 2D plane. The encoder is a LSTM network, and the decoder consists of LSTM and fully connected layers. The attention algorithm is also applied in the Seq2Seq architecture. The PIED model’s encoder is capable of extracting the physical information related to fractures. And the attention module effectively passes on the most relevant physical information related to production to the decoder during the training process. By modifying Seq2Seq architecture, the decoder of the PIED incorporates the intermediate input, which is the constant production time, along with the extracted physical information to predict production values. The PIED model excels in extracting sufficient physical information from high-dimensional inputs while ensuring the integrity of the production time information. By considering the physical constraints, the model predicts production values with improved accuracy and generalization capabilities. In addition, a multi-layer perceptron (MLP) which is broadly used as a proxy model; a regular Seq2Seq model (LSTM–Attention–LSTM); and the PIED were compared via a case study, and their MAE values were shown to be 241.76, 184.07, 168.81, respectively. Therefore, the proposed model has higher accuracy and better generalization ability. In the case study, a comparative experiment was conducted by comparing LSTM–MLP (with an MAE of 221.50) and LSTM–LSTM to demonstrate that using LSTM as the decoder structure is better for predicting production series. Moreover, in the task of predicting production sequences, LSTM outperforms MLP. The Seq2Seq architecture demonstrated excellent performance in this problem, and it achieved a 48.4% reduction in MSE compared to MLP. Meanwhile, the time cost for build datasets was considered, and the proposed model was found to be capable of training in a small dataset (e.g., in the case study, 3 days were used to generate 450 samples for training.); thus, the proposed model has a certain degree of practicality.

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Fast-Track Completion Decision Through Ensemble-Based Machine Learning

Cited by 8 publications

References 22 publications

Machine Learning in Reservoir Engineering: A Review

Machine Learning in Reservoir Engineering: A Review

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

A Physics-Informed Neural Network Approach for Surrogating a Numerical Simulation of Fractured Horizontal Well Production Prediction

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