Multivariate Statistical Analysis Improves Formation Damage Remediation

Tague, J.R.

doi:10.2118/63004-ms

Cited by 9 publications

(3 citation statements)

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“…PCA has been used extensively in geosciences for facies estimation (Doveton 1994;Guo et al 2006;Singh and Carigalli 2007). Also, it has been applied to other oilfield-related tasks, such as the estimation of fault orientation (Tingdahl 1999), selection of strategies for the improved remediation of formation damage (Tague 2000), the characterization and prediction of reservoir properties through reparameterization (Zhu et al 1999;Lee et al 2002;Rwechungura et al 2011), and the design of hydraulic-fracture treatments (Aoudia et al 2010;Kazakov and Miskimins 2011). To our knowledge, this approach has not been used for the analysis of production data from unconventional gas resources.…”

Section: Objective and Methodologymentioning

confidence: 99%

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Bhattacharya

Nikolaou

2013

SPE Journal

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Unconventional gas resources have dramatically changed the future energy landscape. Developing these resources involves substantial risk. Such risk can be mitigated if information gathered at initial stages of the development of a field is used efficiently and effectively to guide future development. A variety of tools-such as decline-curve analysis (DCA), type-curve analysis, simulator history matching, and artificial intelligence (AI)-is used to that effect. These tools accomplish partially overlapping but different tasks. Additional tools that could not only facilitate the analysis of historical data but also guide future development would be of value. In this work, we propose an efficient methodology that can use historical production data from existing wells to answer questions such as the following: Which wells will behave similarly? Which wells will behave differently from each other or from standard expectations? Which factors will contribute to these differences? How can data from existing wells be used to anticipate the performance of new wells? The proposed methodology relies on standard principal-component analysis (PCA) and principal-component regression (PCR). The application of PCA to historical production data from twelve wells in the Holly Branch field quickly identified wells with distinct behavior. The subsequent investigation of pressure and the completion data for these wells revealed reasons for such distinct behavior. Finally, a simple linear model was built with PCR, with good ability to predict production from new wells, as assessed through cross-validation. The value of the efficiency offered by the proposed methodology would be much higher for larger data sets, for which manual analysis of production data is more cumbersome.

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Section: Objective and Methodologymentioning

confidence: 99%

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Bhattacharya

Nikolaou

2013

SPE Journal

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“…In this sense, it is introduced the concept of multivariable statistical analysis by which, through correlations matrixes, the most critical variables are identified to formulate a successful treatment, and by multiple regressions, possible results can be predicted. This methodology comes in handy in wells damaged by multiple types of damage, because of the statistical treatment that can identify the most significative variables, and based on this, formulate treatment in the most effective way [41].…”

Section: Formation Damage Disaggregationmentioning

confidence: 99%

Disaggregation and discretization methods for formation damage estimation in oil and gas fields: an overview

Medina

Castaño-Correa²,

Caro-Vélez

et al. 2020

DYNA

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Formation damage could potentially impede production and injection operations. Hence, characterization and discretization processes of formation damage should be connected to quantification and disaggregation techniques, relying on characterization fundamentals that consider chemical and physical changes in the fluid and rock system through the field productive life. This document presents a review of different disaggregation, quantification and discretization methods for the formation damage estimation in oil and gas fields. This review is mainly divided into three main sections, namely: i) Formation damage diagnosis, ii) Formation damage quantification, and iii) Formationdamage disaggregation. This document will aid in the alignment of the academic and industrial sectors to incentivize the prevention and inhibition of formation damage, as well as the optimal design of remediation mechanisms.

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“…The use of statistics and machine learning to predict the success rate of acidizing has been widely reported in the present literature. For instance, Tague (2000) used multivariate statistical analysis to improve formation damage remediation.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning and Multiple Regression Approach to Predict Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation

Kurniawan

Azis

Ariyanto

2023

ASEAN J. Chem. Eng.

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The success rate of matrix acidizing in hydraulic fractured sandstone formation is less than 55%, much lower compared to the more than 91% success rate in carbonate formation. The need for alternative approaches to help the success ratio in matrix acidizing is crucial. This paper demonstrates a modeling technique to improve the success ratio of matrix acidizing in a hydraulic fractured sandstone formation. Supervised machine learning with 4 models of a neural network, logistic regression, tree, and random forest was selected to predict the successfulness of matrix acidizing in hydraulic fracturing. In parallel, multivariate analysis of principal component regression and partial least square regression approach were utilized to predict the oil gain of the job. For qualitative prediction, the results showed that the random forest was the best model to predict the successfulness of the job with the area under the curve (AUC) of 0.68 and precision of 0.73 in the training model with 70% of the data. Subsequently, the validation test with the rest of the data (30% data) gave 0.51 AUC and 61% precision. For quantitative prediction, the net oil gain was evaluated by using principal component regression (PCR) and partial least square regression (PLS-R). The PCR and PLS-R model gave a coefficient of determination (Rsquare) of 0.22 and 0.35, respectively. The p-value of PLS-R was 0.047 (95% confidence interval) which indicates that the model is significant. The results of this work demonstrate the potential application of supervised machine learning, principal component regression, and partial least square regression to improve candidate selection of oil wells for matrix acidizing especially in hydraulic fractured wells with limited design data.

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Multivariate Statistical Analysis Improves Formation Damage Remediation

Abstract: TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe design and implementation

Cited by 9 publications

References 1 publication

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods

Disaggregation and discretization methods for formation damage estimation in oil and gas fields: an overview

Supervised Machine Learning and Multiple Regression Approach to Predict Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation

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