Impact of Completion System, Staging, and Hydraulic Fracturing Trends in the Bakken Formation of the Eastern Williston Basin

LaFollette, Randy F.; Holcomb, William David; Aragon, Jorge

doi:10.2118/152530-ms

Cited by 19 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the development characteristics of the shale reservoir (fast and intensive development process of numerous wells), it is impossible to obtain rock properties, porosity and permeability and reservoir pressure from all wells by performing both logging and well tests. Research was also carried out for the purpose of applying the coordinate well to replace or supplement geological factors [45][46][47]. In this study, a DNN model was developed that considers well direction and well status, which are categorical variables.…”

Section: Dataset Preparationmentioning

confidence: 99%

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Han

Kwon

2021

Energies

View full text Add to dashboard Cite

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs.

show abstract

Section: Dataset Preparationmentioning

confidence: 99%

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Han

Kwon

2021

Energies

View full text Add to dashboard Cite

show abstract

“…The program is described by Eaton et al (2018); it consists of four North-South oriented wells with 218 stages stimulated over 40 days (Figure 2). One of the four wells was completed with a high-density completion (Lafollette et al, 2012) first, followed by the others. An issue with the microseismic array occurred during the treatment of the later three wells, so only 57 of the 93 stages were recorded.…”

Section: Datamentioning

confidence: 99%

Separating Hydraulic Fracturing Microseismicity From Induced Seismicity by Bayesian Inference of Non‐Linear Pressure Diffusivity

McKean

Priest

Dettmer

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Understanding subsurface fluid propagation and containment is fundamental to safe and effective hydraulic fracturing, wastewater disposal (Elsworth and Spiers, 2016), carbon sequestration (Vilarrasa et al., 2019), and geothermal development (Zang et al., 2014). In the context of hydraulic fracturing, operators need to understand the stimulated reservoir (i.e., in-zone fluid propagation) to assess operational efficiency, while simultaneously mitigating out-of-zone fluid propagation that may result in induced seismicity (IS). Natural fractures, which include faults at a variety of scales (Nelson, 2001), are a major contributor to out-of-zone fluid propagation and IS. The activation of existing faults causes IS, which can result in inefficient energy or thermal reservoir stimulation and potentially hazardous ground shaking.Microseismic monitoring data acquired during hydraulic stimulation provide a wealth of information on fluid migration in the subsurface and are widely used to characterize hydraulic fracture (HF) propagation away from a wellbore in applications such as unconventional resource production and enhanced geothermal systems (Shapiro et al., 2006). The HF propagates as a Mode I tensile fracture and microseismic events are caused by secondary shear fracturing around the primary HF and fluid leakoff (Maxwell et al., 2009). Events can also be induced far from the HF due to poroelastic effects or triggering of shear failure of natural fractures due to propagation of pore fluid through natural fractures.It is advantageous to separate microseismic point clouds into HF and induced events. This allows HF-related microseismic events to be used to calculate the HF dimensions for each hydraulic fracturing stage (Hummel & Shapiro, 2012;Sayers & Calvez, 2010). The induced events can be used to identify tectonic structures and natural

show abstract

“…Moreover, artificial intelligence techniques have been implemented to design and optimize hydraulic fracturing treatments. The number of stages, the volume of the proppant and fluids, the types of chemical additives, and sweet spot identification can be optimized using AI techniques. − However, most of the AI-developed models ignore important geological and reservoir properties such as formation permeability or the presence of natural fractures within the treated formations. Wang and Chen applied different AI tools to predict the productivity of hydraulically fractured wells produced from unconventional tight reservoirs.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

2021

View full text Add to dashboard Cite

Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young’s modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

show abstract

Impact of Completion System, Staging, and Hydraulic Fracturing Trends in the Bakken Formation of the Eastern Williston Basin

Cited by 19 publications

References 1 publication

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Separating Hydraulic Fracturing Microseismicity From Induced Seismicity by Bayesian Inference of Non‐Linear Pressure Diffusivity

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

Contact Info

Product

Resources

About