Analysis of Data from the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques

Awoleke, Obadare O.; Lane, Robert H.

doi:10.2118/127919-ms

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Studies by others [Awoleke and Lane, 2010] and our own work have demonstrated that a few variables have greater effect than others. Horizontal/vertical orientation, obviously, and local geology are among the most important factors.…”

Section: Choosing Analogsmentioning

confidence: 45%

Practical Aspects of Reserve Determinations for Shale Gas

Strickland¹,

Purvis²,

Blasingame

2011

All Days

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Shale gas currently provides 20% of domestic supply, is targeted by half of the gas-directed drilling rigs, and represents the large majority of domestic resources. However, modern shale plays, their development strategies and their engineering analysis are young by comparison to those of conventional reservoirs. Uncertainty in shale gas reserves has significant implications at both the micro and macro levels.Conventional reservoir engineering tools must be viewed as potentially inadequate (or even inappropriate) for the evaluation of shale gas performance primarily because of the extremely low aggregate permeability of these systems, but also because of other unique aspects of the systems. Reservoir modeling (simulation) has an important role as an assessment and prediction tool; however, the character of the reservoir (induced and enhanced natural fractures) must be considered, as well as the geological and fluid characteristics. Rate-transient analysis (modern decline analysis) techniques are also more rigorous and have been expanded and adapted to fit the uniqueness of shale gas production. Application of each method for shale gas is discussed, including methods and limitations. These two techniques more closely represent the physics of shale gas production, but their implementation is often prohibitive.By way of necessity, much engineering evaluation is performed using Arps decline curve analysis. This technique is argued by some to be inappropriate due to a lack of theoretical support and demonstrated tendency to over-estimate reserves in tight gas systems. Given the limitations, practical methods exist to reduce error associated with its use. A newer decline method, power-law exponential, is also investigated.

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Section: Choosing Analogsmentioning

confidence: 45%

Practical Aspects of Reserve Determinations for Shale Gas

Strickland¹,

Purvis²,

Blasingame

2011

All Days

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“…Mohaghegh (2000) published an excellent summary of the application of neural networks in petroleum engineering. A more recent summary is also available (Awoleke 2009). Neural networks (feed-forward type) usually consist of an input layer (number of nodes in the input layer depends on the dimensionality of the data set), one or more hidden layers (number of layers/neurons is dependent on problem complexity), and one output layer (number of neurons dependent on the number of output variables).…”

Section: Water-production Sources and Mechanisms In The Barnett Shalementioning

confidence: 99%

Analysis of Data From the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques

Awoleke

Lane

2011

SPE Reservoir Evaluation &Amp; Engineering

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A Barnett shale water-production data set from approximately 11,000 completions was analyzed using conventional statistical techniques. Additionally, a water/hydrocarbon ratio and first-derivative diagnostic-plot technique developed elsewhere for conventional reservoirs was extended to analyze Barnett shale water-production mechanisms. To determine hidden structure in well and production data, self-organizing maps and the k-means algorithm were used to identify clusters in data. A competitive-learning-based network was used to predict the potential for continuous water production from a new well, and a feed-forward neural network was used to predict average water production for wells drilled in Denton and Parker Counties, Texas, of the Barnett shale.Using conventional techniques, we concluded that for wells of the same completion type, location is more important than time of completion or hydraulic-fracturing strategy. Liquid loading has potential to affect vertical more than horizontal wells. Different features were observed in the spreadsheet diagnostic plots for wells in the Barnett shale, and we made a subjective interpretation of these features. We find that 15% of the horizontal and vertical wells drilled in Denton County have a load-water-recovery factor greater than unity. Also, 15 and 35% of the horizontal and vertical wells drilled, respectively, in Parker County have a load-recovery factor greater than unity.The use of both self-organizing maps and the k-means algorithm showed that the data set is divided into two main clusters. The physical properties of these clusters are unknown but interpreted to represent wells with high water throughput and those with low water throughput. Expected misclassification error for the competitive-learning-based tool was approximately 10% for a data set containing both vertical and horizontal wells. The average prediction error for the neural-network tool varied between 10 and 26%, depending on well type and location.Results from this work can be used to mitigate risk of water problems in new Barnett shale wells and predict water issues in other shale plays. Engineers are provided a tool to predict potential for water production in new wells. The method used to develop this tool can be used to solve similar challenges in new and existing shale plays. IntroductionLarge bodies of data relating to fracturing operations in gas shales exist in public databases. These databases also contain production data. In this work, we first examine production figures from the Barnett shale using conventional statistical techniques to identify trends and extract meaningful information from these trends.Second, some work has been conducted on characterizing water-production mechanisms in conventional hydrocarbon reservoirs on the basis of the analysis of water/oil-ratio (WOR) and water/gas-ratio (WGR) data over time (Chan 1995). This technique has not been applied yet to understanding the water-production mechanisms in unconventional gas reservoirs. Therefore, we

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“…ANN has achieved significant popularity in areas such as production prediction , reservoir characterization or properties prediction (An et al 1993, Goda et al 2003, Tang et al 2011, history matching (Ramgulam 2006), classification (Stundner et al 2001), proxy for prediction of recovery performance (Lechner et al 2005, Awoleke et al 2011, production operation optimization and well design (Stoisits et al 1999, Yeten et al 2002, Ayala H et al 2007). In recent years, the neural network has also been utilized to evaluate enhanced oil recovery (EOR) projects (Zerafat et al 2011, Parada et al 2012, predict heavy oil recoveries (Ahmadloo et al 2010, Popa et al 2012, and assess CO 2 sequestration process (Mohammadpoor et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Practical Implementation of Knowledge-Based Approaches for SAGD Production Analysis

Leung

Zanon

et al. 2014

Day 1 Tue, June 10, 2014

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Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic and computationally demanding, and it not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain.In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly-available sources is studied. Exploratory data analysis is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural networks (ANN) are employed to facilitate SAGD production performance prediction. Predicting (input) variables including porosity, net-to-gross ratio, saturation, gross pay, normalized shale barrier thickness and distance to well pair, and initial production rate are formulated. Measures such as cumulative production over discrete time intervals are considered as prediction (output) variables. Data records that are comprised of both input and output variables are assembled; the network is trained using the data set to identify all significant patterns and relationships that exist between the input and the output variables. The model is subsequently validated using a cross-verification procedure, during which records that have been excluded at the training stage are presented to the model. This paper demonstrates that knowledge-based techniques can be implemented in a practical manner to analyze large amount of competitor data efficiently. The approach can be integrated directly into most existing reservoir management routines. It can also be readily updated when new information has become available. Given that robust reservoir management and real-time decision-making are major challenges faced by the industry, the data-driven models presented in this paper has great potential to be applied in other recovery projects such as solvent-aided steam injection.

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Analysis of Data from the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques

Cited by 8 publications

References 15 publications

Practical Aspects of Reserve Determinations for Shale Gas

Practical Aspects of Reserve Determinations for Shale Gas

Analysis of Data From the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques

Practical Implementation of Knowledge-Based Approaches for SAGD Production Analysis

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