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Abstract Distributed Fiber Optics (DFO) technology has been the new face for unconventional well diagnostics. This technology focuses on measuring Distributed Acoustic Sensing (DAS) and Distrusted Temperature Sensing (DTS) to give an in-depth understanding of well productivity pre and post stimulation. Many different completion design strategies, both on surface and downhole, are used to obtain the best fracture network outcome; however, with complex geological features, different fracture designs, and fracture driven interactions (FDIs) effecting nearby wells, it is difficult to grasp a full understanding on completion design performance for each well. Validating completion designs and improving on the learnings found in each data set should be the foundation in developing each field. Capturing a data set with strong evidence of what works and what doesn't, can help the operator make better engineering decisions to make more efficient wells as well as help gauge the spacing between each well. The focus of this paper will be on a few case studies in the Bakken which vividly show how infill wells greatly interfered with production output. A DFO deployed with a 0.6" OD, 23,000-foot-long carbon fiber rod to acquire DAS and DTS for post frac flow, completion, and interference evaluation. This paper will dive into the DFO measurements taken post frac to further explain what effects are seen on completion designs caused by interferences with infill wells; the learnings taken from the DFO post frac were applied to further escalate the understanding and awareness of how infill wells will preform on future pad sites. A showcase of three separate data sets from the Bakken will identify how effective DFO technology can be in evaluating and making informed decisions on future frac completions. In this paper we will also show and discuss how DFO can measure real time FDI events and what measures can be taken to lessen the impact on negative interference caused by infill wells.
Abstract This paper presents the results of a Material Balance Study in the Mene Grande field, in the Lake of Maracaibo of the region of Venezuela. The study represents the continuation of ongoing subsurface evaluations, which have been the subject of previous publications. The identification and confirmation of the major drive mechanisms and source of water encroachment in the field has led to successful use of traditional Material Balance techniques, where different areas are selected in order to determine the viability ofinfill well locations. Prior to obtaining a clear understanding of the subsurface drive mechanisms, infill drilling was based on neighbor well production and reservoir thickness. The results of the infill locations based on this type of evaluation were highly variable and somewhat disappointing. Although simulation modeling was used initially, subsequent well results indicated large discrepancies between the static models and the actual field results. Once these discrepancies had been reconciled, a consistent understanding of the reservoir performance began to develop. This paper presents the results of applying material balance techniques to reservoir sectors incorporating entire production history and pressure of the neighboring wells to the future infill target. Each sector was matched to the observed pressure, which again showed signs of a secondary support mechanism developing from the reservoir mudstones. It is in fact this secondary pressure support, which allows for higher production from the recent infill wells. A comparison of the completed wells using this methodology and the wells that were located without it is shown indicating a clear and significant benefit to production rates. This methodology can be considered as a first step towards the construction of a full field 3D model. The proposed methodology is simple and easy to use. This case study clearly demonstrates, that subsurface success depends more on correct understanding of the reservoir rather than the degree of complexity with which it is modeled. Introduction Mene Grande was the very first oil field discovered in Venezuela in 1914, with the drilling of the Zumaque-1, which is still producing 20 bopd. The Field is situated approximately 120 km southeast of Maracaibo City on the eastern side of the Lake Maracaibo Basin (Fig. 1). The field extension is 123 km2 and 945 wells have been drilled to date since the field was discovered. The field comprises a heavy oil Miocene reservoir with overlying stratified tar sands within the 1200–1500ft thick Isnotu Formation. The Isnotu Formation is bounded above and below by unconformities and lies unconformably upon sands and muds of the Eocene Pauji Formation. The reservoir comprises unconsolidated fluvial channel sands and crevasse splays, bounded by poorly indurated muds and silts. This formation is a combination of three main levels, the "K" sands, the "KLM" clays and the "LM" sands. The overall net sand/gross percentage in the total reservoir is generally low to moderate varying between 10 and 35%. The oil found in the Miocene formation has an API varying between 12 and 20 and has a viscosity between 10–30 cp. Most wells are producing under artificial lift conditions (Rod Pump and PCP).
Abstract Infill drilling has been identified to play an important role in improving oil or gas recovery in the tight hydrocarbon fields. But quantifying the infill drilling potentials in producing tight hydrocarbon fields is often a challenging problem, due to large variability in rock quality, well spacing, well completion practices, and the large number of wells involved. At the same time, with the increasing demand for energy and favorable oil and gas prices, more and more fields all over the world are undergoing infill drilling. It is not uncommon for a company to have hundreds or thousands of infill candidates to choose from. Complete integrated reservoir studies to determine infill potential are often very time-consuming and costly for many tight hydrocarbon fields. This paper presents an alternative approach to conduct detailed reservoir studies to find infill drilling potentials in tight hydrocarbon basins. This method can quickly evaluate the infill drilling potentials within weeks even with thousands of wells. Several field examples are provided to demonstrate the application of the method in mature-tight gas basins and the primary advantages of the technique are its speed and reliance upon well location and production data only. Introduction As the world's supply of conventional oil and gas reservoirs begins to deplete in the 21st century, the world will demand that oil and gas production from unconventional reservoirs increase to help supply keep up with demand. One of the keys to ensuring an abundant, economical, and environmentally-friendly supply of natural gas is developing the tremendous gas resources in unconventional, low-permeability, tight reservoirs. Although the average production rate of gas wells from the unconventional, low-permeability, tight reservoirs is low, they still play an important role in the petroleum industry. According to the 2003 released Interstate Oil and Gas Compact Commission (IOGCC) survey, the tight gas wells collectively represent 10 percent of the natural gas produced onshore in the lower 48 states which accounts for 43 percent of the overall rise in natural gas production over the year 2002, and tight oil production increased by 7,678,000 barrels in 2002 to 323,777,000 barrels while the overall domestic onshore oil production declined. However, the low-permeability wells are being produced not by major oil companies, for the most part, by small independent operators. Research is the key to the survival of those low-permeability wells; however, those small independent producers do not have the means to conduct their own research. Faced with the daunting task of trying to determine where to drill several hundred infill wells in a tight-gas reservoir, many operators have to rely on very simple analyses to select infill locations. This often results in wells being drilled in the wrong locations, and even worse, after seeing less-than-expected infill performance, many operators will simply give up on infill drilling and this may be missing significant opportunities. Infill drilling of additional wells after initial development (primary and/or secondary) played an important role in improving the oil and gas recovery in the tight hydrocarbon reservoirs. Generally speaking, the reservoir heterogeneity and layer continuity can be changed by the well spacing. The infill drilling wells reduce the well spacing of the hydrocarbon fields and then enhance the well connectivity. Wu, et al. reported the results of their study to determine the impact of infill drilling on the waterflood recovery in West Texas carbonate reservoir. Their study shows a certain degree of correlation between the waterflood recovery and well spacing. Recently, with the increasing demand for energy and favorable oil and gas prices, more and more fields all over the world are undergoing infill drilling. Although the advances in reservoir management provides a much clear picture of hydrocarbon distribution in the reservoirs which helps petroleum engineers to plan highly effective well profiles. The advanced imaging technologies allow the hydrocarbon field operators to select the best locations for infill drilling to optimize well placement. But what happens when we face a mature tight field which has hundreds or even thousands of developing wells? Usually, it is not uncommon for a company to have hundreds or thousands of infill candidates to choose from in the tight hydrocarbon fields.
Abstract Quantifying the drilling or recompletion potential in producing gas basins is often a challenging problem, due to large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time-consuming and costly for many producing gas basins. In this work we evaluate the accuracy of a statistical moving window technique that has been used in tight gas formations to assess infill and recompletion potential. The primary advantages of the technique are its speed and its reliance upon well location and production data only. The statistical method was used to analyze simulated low-permeability, 100-well production data sets; moving window infill well predictions were then compared to those from reservoir simulation. Results indicate that moving window infill predictions for individual wells can be off by more than 50%; however, the technique accurately predicts the combined infill production estimate from a group of infill candidates, often to within 10%. The accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. The cases evaluated in this study included real-world well spacings and production rates and a significant amount of depletion at the infill locations. Due to its speed, accuracy and reliance upon readily available data, the moving window technique can be a useful screening tool for large infill development projects. Introduction The most accurate way to determine infill-drilling potential in a gas basin is to conduct a complete reservoir evaluation involving geological, geophysical, and reservoir analyses and interpretations. This includes developing a geological model of the study area, estimating distributions of static reservoir properties such as porosity and permeability, constructing and calibrating a reservoir simulation model of the area, and then using the reservoir model to predict future production and reserves at potential infill well locations. While it may be accurate, this approach can be prohibitively time-consuming and expensive. For some large, low-permeability gas basins with large data sets (sometimes over 1,000 wells) and complex geology, the cost and time requirements of a conventional reservoir evaluation study are not acceptable. McCain et al. used a statistical, moving-window method to determine infill potential in a complex, low-permeability gas reservoir. Later, Voneiff and Cipolla applied a similar method to analyze well location and production data for rapid assessment of infill and recompletion potential in the Ozona field. While the feasibility and value of this approach has been demonstrated, a systematic assessment of the validity and accuracy of the technique has not been presented in the literature. The objective of this work was to quantify the accuracy of the moving window technology for selecting infill candidate wells in low permeability gas reservoirs. We did this by calculating infill well performance with the moving window method from simulated data, from which we could readily determine the "best" infill candidates for comparison.