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Development of an Excavation Damaged Zone around an underground excavation can change the physical, mechanical and hydraulic behaviours of the rock mass near the underground space. This paper presents an approach to build a prediction model for the assessment of EDZ based on an artificial intelligence method called artificial neural networks which are applied to build a prediction model for the assessment of EDZ using data of geological and blasting parameters which are chosen as a result of a literature review. Upon developing the model to evaluate rock damage from underground blasts, practical applications were accomplished for confirmation. Results showed that, because of their high accuracy in establishment of a correlation between EDZ and input parameters' data, ANNs are appropriate tools to predict excavation damaged zone using data of parameters including perimeter powder factor, rock mass quality, tensile strength, density, wave velocity, vibration propagation coefficients and explosive detonated per delay. 1 INTRODUCTION The extent of excavation damaged zone depends on geological structure, excavation method, overburden, and numerous other parameters. Prediction of this damage is an important factor to evaluate the quality of excavation process in tunnelling and underground mining. It would allow the optimization of explosive charges utilized in successive blasting rounds, as well as lowering risks of instability from rock loosening, less support costs and water inflows. The detonation of explosives confined in boreholes generates a large volume of gases at high pressures and temperatures. The sudden application of these effects to the cylindrical surface of the hole generates a compressive stress pulse in the rock, which may be a source of damage in the surrounding zone. The dimensions of that zone depend on the size of explosive charge detonated, rock's dynamic strength and density, wave velocity propagation, and vibration velocities transmitted to the rock mass. The detonation of explosives confined in boreholes generates a large volume of gases at high temperatures (2000–5000°C) and high pressures (10–40 GPa). The sudden application of these effects to the cylindrical surface of the hole generates a compressive stress pulse in the rock, which may be a source of damage in the surrounding zone. These deviations are normally undesirable because they generate higher costs in the constructive process of the underground opening [Dinis Da Gama, et al., 2002].
Nejad, Amir M. (StrataGen Engineering) | Shelley, Robert F. (StrataGen Engineering) | Lehman, Lyle V. (StrataGen Engineering) | Shah, Koras (StrataGen Engineering) | Gusain, Deepak (StrataGen Engineering) | Conway, Matthew T. (StrataGen Engineering)
Abstract This paper discusses the workflow for developing a model to predict gas production for hydraulic fracture stages in a brittle shale environment. In our last paper (SPE 163829), we developed a model to predict fracture network geometry (width, length, and height) obtained from microseismic data for various fracture treatment designs. In this study, using both microseismic data and fracture/completion data; frac treatment production is estimated. The well database used for this purpose is comprised of fracture design parameters including treatment volumes, rates, proppant mass and size; well properties include perforation interval length, and perforation depth. The goal of this study is to provide insight into factors affecting well production in a brittle shale environment. Initial data screening demonstrated massive data scattering and as a result data mining techniques are employed to find possible hidden relationships to explain the nature of the data. Also, using sensitivity analysis on the predictive model, improvements in the current fracture designs and completion schemes in Barnett shale are made. The database is examined from different aspects using various data mining approaches. After screening and preprocessing the data, non-process affected outlier wells are removed from the dataset. Then, a forward predictive neural network model is trained with fracture design and well data parameters as inputs and well cumulative gas production per stage as outputs. Neural networks are trained with the help of genetic algorithm (GA). The sensitivity study on the trained network provided many insights about well completion and stimulation strategies. Recommendations on how to improve fracture designs and well completion schemes are provided based on sensitivity analysis on the neural networks. Results of neural network modeling in Barnett shale are compared to other gas producing shale assets such as Fayetteville and Haynesville shale to compare the findings. The results of this work potentially help understanding of completion and fracture treatment designs on well productivity in gas producing shale assets. This will potentially help operators understand how to more effectively design frac treatments and/or reduce the operational costs associated with well completion in a brittle shale environment. Considering the fact that the relationship between stimulated reservoir volume and production is not entirely understood, this work may shed some lights on the aforementioned issue.
Popa, Andrei (Chevron North America Exploration and Production) | Sivakumar, Kailash (University of Southern California) | Hao, Minshen (University of Southern California) | Cassidy, Steve (Chevron North America Exploration and Production)
Abstract The California's Monterey formation is thought to hold significant hydrocarbon potential and is looked upon as a long term opportunity for development. Given its unique depositional environment, digenesis and very low permeability the hydrocarbon bearing formations have been referred as unconventional reservoirs. Since early 1900's the San Joaquin Valley's Monterey formation has been targeted and produced by several determined and ambitious operators. Still today, these operating companies are trying to develop an understanding of how to best develop and economically unlock this potential to further exploit the resource. Different stimulation techniques, such as hydraulic fracturing and acid treatments, have been used in an attempt to unlock the resource. It was observed that, while hydraulic fracturing was to some extent effective in several fields, other reservoirs have been found to respond very well to acidizing treatments. The wealth of acid treatment data and corresponding production response from the Monterey formations present a great analysis opportunity to identify best practices and optimize well completions to maximize production. This paper presents a study undertaken to analyze the acid stimulation treatments in one of the structures of the Monterey formation and consists of two parts. The first part covers the descriptive analysis and involves the use of supervised and unsupervised clustering techniques to evaluate the relationship between acid treatments and production data. Its objective is to understand the success of the treatments and to identify acid job best practices. In the second part, using only the available information and cluster knowledge extracted, an attempt was made to develop a predictive tool to forecast the performance of new/infill wells and re-stimulation treatments. The study revealed a relatively good understanding of the factors affecting the production response of individual wells and also the variations observed in the different parts of the field. Shortly after the completion of the study, the operating company executed similar activities to what the study found, unbeknownst to the authors. The positive results achieved were not only a validation of the recommendations but also demonstrated the business value created by such analyses.
Marin, Horacio Daniel (Tecpetrol S.A.) | Valencio, Daniel (Tecpetrol S.A.) | Muruaga, Enrique (Tecpetrol S.A.) | Shelley, Robert Frank (Halliburton Energy Services Group) | Sorenson, Federico | Tiffin, Judy Lynn (Halliburton Co.)
Abstract This paper presents a description of the process used to evaluate data from the Comodoro Rivadavia and Mina El Carmen zones in the El Tordillo Field in Argentina. The evaluation presented is holistic in nature and was performed by a team of experts. This evaluation consists of field-data quantification, integration, statistical and visual analysis, and development of a predictive artificial neural network (ANN) model capable of identifying sands with commercial hydrocarbon potential. The ANN model was used to identify patterns and trends related to the geology, reservoir, well-drilling issues, and swab-test production result. The purpose of this analysis was to identify sand attributes that are indicators of the sand's productive fluid type and capability to produce. This process was used to resolve difficult petrophysical interpretation issues associated with a complex sandstone reservoir system. The derived ANN models were subjected to a blind test. These models were accurate 86% of the time at predicting oil/water ratios. The models were used to rapidly evaluate 149 sands in two new wells and have proven useful in the evaluation of new-well oil-production potential. Background El Tordillo Field. The El Tordillo field was discovered in 1932. It covers an area of approximately 45.2 square miles (117 square kilometers). The field has three main productive formations: El Trebol, Comodoro Rivadavia, and Mina El Carmen. As of June 2002, the field had produced approximately 34 million m oil, and 4.32 Bcf gas from these three formations. As of June 2002, a total of 1,089 wells had been drilled in the El Tordillo field. Tecpetrol drilled 245 wells. Currently, 546 wells are in production and 1,226 workovers have been or are now being performed field-wide. The field also has 135 wells injecting 32 700 m/d water to help increase oil production. Challenges. The Comodoro Rivadavia and Mina El Carmen formations comprise a complex succession of sandstones and shales deposited during the Cretaceous Age. Gross thickness is in excess of 3,640 ft (1110 m) in the El Tordillo field. The formations can be divided into seven production markers. Deposited under different depositional environments, the sandstones of each marker have characteristic petrophysical attributes, but in general, exhibit diverse reservoir-rock qualities. The porosities and permeabilities vary greatly throughout the field. Years of exploitation of the complex and layered reservoirs of the El Tordillo field have resulted in a number of undrained, low-permeability, or severely damaged zones with significant hydrocarbon reserves. The conventional petrophysical analysis using a conventional log alone is not sufficient to define the zone candidate to perforate (salinity of the formation water varies from sand to sand). To produce these candidate zones in a profitable way, identification of sands with production enhancement potential are required. In addition to these zones, near-depleted zones require extension of production life. Objective of Analysis. The objective of the ANN effort was to analyze mud logging, pressures, geological correlations, oil gravity, swab tests, and electric logs to identify prospective zones. Based on this data we built multivariable analysis ANN models to determine those parameters that will identify, with a high degree of certainty, promising candidates for simple or multiple fracture treatments. As part of this study, two ANN models have been developed for the purpose of identifying promising sand candidates. Each of these models is trained to predict a specific aspect of an actual swab-test result. The swab-test fluid-inflow (STFI) model uses drilling parameters and sand characteristics to predict whether a sand has the potential to produce fluid inflow into the wellbore. The swab-test water-cut model (STWC) uses geologic information about the sand, along with log-derived characteristics to predict an expected percent water fraction that will be produced from a sand. The accuracy of these models was 67% and 86% respectively, in the blind test performed.
Abstract In the studied oil filed, the sanding problems have been recognized to intensify in 1990s when water production has become substantial. It is predicted that water production will increase due to the driving mechanism and its reservoir fluid and rock characteristics. It is important to study and analyze the formation's sand production potential at this stage of the reservoir development, and formulate a sand control strategy in order to optimize the field operation. In sand prediction, one of the most indicative parameters is UCS (Unconfined Compressive Strength) of reservoir rocks. In order to improve the estimation of UCS profile in the target sandstone interval, we employed a back-propagation neural network which relates the rock strength and well log responses. The errors in the UCS estimation were examined by changing structure of the network, combination of reference well log types, and selection of the actual UCS learning data. Some combinations of the well logs with sonic log generated a high accuracy estimation of UCS data. Using the UCS data thus estimated, the stability of perforation cavities under anticipated production condition was investigated with a FEM numerical model. Based on the sensitivity of the operational conditions, the results demarcated the reservoir rock conditions into "safe", "manageable", and "catastrophic" regions1 with more confidence than before. This multi-dimensional diagnosis of reservoir rock stability indicated the practical guideline for the well completion through organized logging programs and perforation design (selective/oriented) to minimize the sand risk. It also suggested the general production constraints of critical draw down in the artificial lift to optimize the production operation. Following the introduction of the study in the first chapter, the second chapter summarizes the formation strength characterization. The formation failure analysis using these rock properties is described in the following chapter. The sand control strategies based on this analysis are briefly summarized as concluding remarks. General: Background of the Study. The studied oil-bearing formation is weakly consolidated, fine-to-medium grained, well sorted sandstone of Middle Cretaceous age and is separated into the upper and lower reservoirs by thick shaly sequences. Both the reservoirs are composed of many stacked channel sands, each being 10-50 ft thick, interbedded by thin shale layers. They have strong natural water drive and their permeability is on Darcy order. After production of over 20 years, water production was recognized at some wells in 1980s and the field-wide water cut is now at the level of 10-15 %. The infill wells have been drilled as horizontal wells since late 1980s and gas lift has been employed at some wells since early 1990s. Sanding was first observed at some these horizontal and gas lift wells. Though the present sand production does not pose a serious operational problems, we believe it necessary at this stage to study and forecast the reservoirs' sand production potential in the future. In the earlier study, it was attempted to identify within the reservoirs the zones prone to sand production by means of a simplistic approach. The Mohr-Coulomb failure criterion was applied without detailed stress-strain analysis and thereby a critical drawdown expression was derived in terms of maximum and minimum in-situ stresses, pore pressure at far field, UCS, and failure angle. The results, however, were later found to be too conservative. To establish a better sand control strategy, this study took the following steps:Measurement of the UCS Correlation of the UCS with petrophysical data Tri-axial testing Formation failure analysis Background of the Study. The studied oil-bearing formation is weakly consolidated, fine-to-medium grained, well sorted sandstone of Middle Cretaceous age and is separated into the upper and lower reservoirs by thick shaly sequences. Both the reservoirs are composed of many stacked channel sands, each being 10-50 ft thick, interbedded by thin shale layers. They have strong natural water drive and their permeability is on Darcy order. After production of over 20 years, water production was recognized at some wells in 1980s and the field-wide water cut is now at the level of 10-15 %. The infill wells have been drilled as horizontal wells since late 1980s and gas lift has been employed at some wells since early 1990s. Sanding was first observed at some these horizontal and gas lift wells. Though the present sand production does not pose a serious operational problems, we believe it necessary at this stage to study and forecast the reservoirs' sand production potential in the future. In the earlier study, it was attempted to identify within the reservoirs the zones prone to sand production by means of a simplistic approach. The Mohr-Coulomb failure criterion was applied without detailed stress-strain analysis and thereby a critical drawdown expression was derived in terms of maximum and minimum in-situ stresses, pore pressure at far field, UCS, and failure angle. The results, however, were later found to be too conservative. To establish a better sand control strategy, this study took the following steps:Measurement of the UCS Correlation of the UCS with petrophysical data Tri-axial testing Formation failure analysis
This paper describes the application of multi-variate statistical techniques, discriminant analysis and neural networks in identifying drilling and other completion practices that impact on well productivity. Discriminant analysis determines whether a well can be assigned to a group of wells, on the basis of a number of common characteristics and using linear multivariate correlations. Neural nets enable the use of nonlinear correlations for such a classification.
In this study. 47 gas wells from two fields were classified Into three groups: Group 1: no production; Group 2: production below 5900 std m3/h (5 MMscf/D); Group 3: production over 5900 std m3/h (5 MMscf/D).
The variables used in the discriminant analysis included parameters such as completion type. total height of the perforated interval, mud weight. drawdown during perforation, type of mud and perforation size.
The study has identified and, to some extent, quantified those parameters that either adversely or favorably affect well productivity. parameters that either adversely or favorably affect well productivity. The results can be used to adjust operational procedures to maximize well productivity. The parameters identified as increasing productivity reflect, for the most part. sound engineering practices.
Application of neural nets enables further quantification of the effects of petroleum engineering parameters on well productivity and is being developed to make it possible for the most economical preventive and remedial measures to be selected. preventive and remedial measures to be selected. However, statistical techniques are applicable only when a sufficiently large data base is available, i.e., they are suitable for reasonably large and fairly mature fields and/or areas.
Probably several thousand stimulation treatments are carried out each year with a total expenditure of many millions of US dollars. The prerequisites for successful stimulation are simple: prerequisites for successful stimulation are simple: 1. Does the reservoir Contain sufficient amounts of hydrocarbons? 2. Are reservoir pressure and permeability high enough to move the hydrocarbons from the reservoir towards the wellbore?
In other words there should be a clear indication that substantial gains are possible. i.e., enough producable hydrocarbons should be present to justify the treatment for a particular well. This holds for both present to justify the treatment for a particular well. This holds for both fracturing and matrix treatments.
Hence, the selection of candidates for stimulation treatments requires knowledge about the general performance of oil and gas wells in specific fields or areas. A stimulation campaign should start with the identification of field-wide trends with respect to production-impairing mechanisms, using a statistical evaluation of field production-impairing mechanisms, using a statistical evaluation of field data. At the same time, such a statistical approach can, and often will, produce correlations between the productivity in a field and specific produce correlations between the productivity in a field and specific drilling and completion parameters. This is very useful, for instance, in identifying those practices that will prevent impairment in future wells.