The Marcellus Shale has more than a decade of development history. However, there are many questions that still remain unanswered. What is the best inter-well spacing? What are the optimum stage length, proppant loading, and cluster spacing? What are the ideal combinations of these completion parameters? And how can we maximize the rate return on our investment? This study proposes innovative tools that allow researchers to answer these questions. We build these set of tools by utilizing the pattern recognition abilities of machine learning algorithms and public data from the Southwestern Pennsylvania region of the Marcellus Shale. By means of artificial intelligence and data mining techniques, we studied a database that includes public data from more than 2,000 wells producing from the aforementioned study area. The database contained completion, drilling, and production history information from various operators active in Allegheny, Greene, Fayette, Washington, and Westmoreland counties located in the Southwestern Pennsylvania. Extensive preprocessing and data cleansing steps were involved to prepare the database. Various machine learning techniques (Linear Regression (LR), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Gaussian Processes (GP)) were applied to understand the non-linear patterns in the data. The objective was to develop predictive models that were trained and validated based on the current database. The predictive models were validated using information originating from numerous wells in the area. Once validated, the model could be used in reservoir management decision-making workflows to answer questions such as what are the best drilling scenarios, the optimum hydraulic fracturing design, the initial production rate, and the estimated ultimate recovery (EUR). The workflow is purely based on field data and free of any cognitive human bias. As soon as more data is available, the model could be updated. The core data in this workflow is sourced from public domains, and therefore, intensive preprocessing efforts were necessary.
The interest in exploitation of ultra-low permeability formations, such as the Marcellus Shale, has increased in the recent years. Shale formations require massive stimulation treatments to achieve economic production. The recent advances in horizontal drilling and multistage hydraulic fracturing have proved successful in achieving commercial production. However, the parameters that directly affect lifetime production of the wells have not been well established. The primary objective of this study is to examine the effects of basic stimulation parameters used in hydraulic fracturing of Marcellus Shale wells on production performance. Historical production data and stimulation treatment information have been collected and analyzed for a number of horizontal wells both in West Virginia and Pennsylvania. A commercial reservoir simulator which utilizes a dual porosity model and accounts for adsorbed gas was utilized for history matching and predicting the long term production performance. The impact of the stimulation parameters including number of stages, stage spacing, the volume of water used, and the volume of sand used on production performance was investigated. An understanding of the impact of each stimulation parameter to overall production of a well can be used for the optimization of stimulation treatments.
The scope of this paper is to use publicly sourced data as a starting point for production analysis of two distinct well populations. The well populations that will be examined are horizontal Marcellus Shale wells (1) frac'd earlier in time with a defined plug-to-plug stage spacing and (2) frac'd at the same time or later in time with 1.5 to 3 times the number of stages, a production acceleration technique known as Reduced Cluster Spacing (RCS). Public production databases were used to create snap shots of completion activity over time. To speed work flow, a more robust and regularly updated commercially available database that contains both completion and production data was used. Two distinct populations of wells for analysis were identified. A conservative decline curve analysis (DCA) was made to forecast estimated ultimate recovery (EUR) for all wells using only public data. For selected wells, reciprocal production rate versus square root of time plots were generated. This paper will attempt to answer the following questions: (1) were statistically meaningful populations of both stimulation techniques found to exist; (2) how much production uplift did RCS achieve both early on and later time; (3) was there a real increase in EUR; (4) was there a difference in stimulation efficiency; and, (5) what can we learn by plotting P10, P50, and P90 values and EUR distribution curves for specific well populations. To the authors' knowledge this would be the first SPE paper to explore this topic over such large Marcellus Shale well populations in Pennsylvania.
In the Appalachian Basin, the primary focus has shifted from exploratory wells to full pad development. Therefore, generational affects--the relationship between parent and child wells--are becoming a primary concern to operators that are fully developing their lease positions. This study examined bottomhole gauge data from a parent Marcellus Shale well that was recorded during the hydraulic fracture stimulation of three children wells in the Marcellus Shale and two children wells in the Burket Shale. The characteristics of the children wells created a robust data set due to variation in inter-well spacing, producing formation, completion design, and stimulation timing. Fracture modeling was performed in advance of the completion operations in order to mitigate possible parent-child communication. The parent well produced natural gas and condensate for nine months prior to being shut-in for the completion of the children wells. Rate transient analysis was performed on the parent well to further understand the depletion of the producing zone. Detailed bottomhole pressure and temperature data were measured in the parent well during the stimulation. Once operations were completed, the bottomhole gauge data was examined to identify frac hits to the parent well. The general magnitude and timing of the frac hits were examined in relation to the rock matrix and completion design parameters, and completion sequence. It was concluded that over 75 percent of Marcellus frac stages communicated with the parent well, with the most frac hits being attributed to the nearest child well. Logistic regression tests were performed on individual parameters to determine key influencers on the likelihood of a frac hit. Detailed bottomhole gauge data, as presented in this paper, is limited in the current literature due to the expense of data acquisition. The unique characteristics of the wells involved in this field experiment provide for robust statistical analysis that is not typically available publicly in the Appalachian Basin.
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