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ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
In January of 2020, Corterra Energy, an independent exploration and energy production company with operations in the Midcontinent region of Oklahoma, approached Upwing Energy to analyze some of their unconventional wells to better understand the effect of Upwing's Subsurface Compressor System (SCS) on production and recoverable reserves. The analysis was conducted with the Upwing Enhanced Production Simulator (ESP), a proprietary in-house analysis tool, which takes in consideration the reservoir characteristics, the SCS operational envelop, the wellbore dynamics and the topside equipment. The tool has been developed and validated over the course of five well trials, including the company's most recent trial in Riverside Petroleum's New Albany unconventional shale well with a 5,000-foot horizontal wellbore. The reservoir characteristics of the Riverside Petroleum well are very similar to the selected Corterra wells analyzed below. In general, the estimated ultimate recovery of the original gas in place from unconventional horizontal and multi-staged fractured wells is approximately 15 to 20 percent.
Budding petroleum engineers got their hands dirty on 22 June in a field trip to classic red-sandstone Devonian sites along the coastline northeast of Arbroath, Scotland. The trip was organized by the SPE Aberdeen Young Professionals Chapter. Participants examined reservoir characteristics within the upper and lower sandstones in exposed cliff sections. At Cove Haven, Carlingheugh Bay, and Whiting Ness, the group received a brief introduction to the outcrops and then had the opportunity to review the rocks in detail. Collective observations and interpretations were discussed to gain better understanding of the deposition environment, likely reservoir characteristics, and geometries for model building. Reservoir geometries, reservoir quality, and structural controls (faults) were also investigated to illustrate their impact on reservoirs of a similar age in the central North Sea. The chapter wishes to thank Total and BP for sponsoring the trip.
The wells in the "A" geothermal field located in the Philippines, have high bottomhole temperature (BHT) of 600°F and bottomhole flowing pressure (BHFP) of 2,000 psi. The productive section in this field has "shallow" and "deep" reservoirs which are separated by a low-permeability formation. The interaction between the reservoirs is hence limited except through the wells resulting in intrazonal flows under shut-in conditions. As observed with time, these flows have been upflows making the overall production very stable. However, in recent years, it has been found that the cooler fluid inflow from the shallow reservoir has relatively increased, causing reduction in production levels. Under flowing conditions, this has resulted in both flow instability and downflows in wells, which in turn have decreased the individual well production capacity. In order to activate and enhance well production, coiled tubing (CT) nitrogen lift operations were required to be performed to unload the cold water in geothermal wells, hence enhancing steam production.
The wells in this field are completed with large completion sizes (7-in., 9.625-in., and 13.375-in.) and have high BHT (600°F), which makes conventional coiled tubing operations highly challenging. Because the coiled tubing operations in geothermal wells are limited as compared to the conventional operations, planning and executing these for the first time in the "A" field was challenging operationally and technically. As such, surface equipment failure risk was high, putting at risk successful coiled tubing operations.
To gain further understanding of operations in high temperature and cold water downflow environments, CT simulations were combined with simulations from the geothermal reservoir to overcome modeling limitations. The outcome helped designing a new cooling loop system and allowed optimizing the nitrogen lift technique. As a result, two large-diameter geothermal wells were lifted safely with 2-in. coiled tubing in the Philippines.
Determine formation properties (porosity, saturation, net pay) from well log interpretation. Determine lithology from well logs. Design well testing program to meet guidelines. Determine duration of well test required to measure specific near-well and reservoir properties in homogeneous, isotropic reservoirs. Determine duration of well test required to measure specific near-well and reservoir properties in homogeneous, isotropic reservoirs.
Li, Ke (Clausthal University of Technology) | Samara, Hanin (Eurotechnica GmbH) | Wang, Xuan (Clausthal University of Technology) | Jaeger, Philip (Eurotechnica GmbH) | Ganzer, Leonhard (Clausthal University of Technology) | Wegner, Jonas (Clausthal University of Technology) | Xie, Lin (Southwest Petroleum University)
Abstract Oil shale is the most abundant fossil energy resource discovered in Jordan. The objective of this paper is to investigate reservoir characteristics and evaluate the resource potential of the Sultani oil shale deposit in central Jordan, based on their mineral composition, geochemical characteristics and reservoir microstructures. The samples used for this study were taken from the outcrop in Sultani deposits, South-East of Al-Karak city adjacent to the desert highway. The collected samples were cleaned and made into powder sample, kerogen sample, thin section sample, and ion beam polishing sample. The powder sample was analyzed by X-Ray Diffraction and Organic Carbon Analyzer to clarify the mineral composition and TOC value. The kerogen samples were tested for evaluate the kerogen type and maturity of organic matter. The thin section and ion beam polishing sample were examined by Optical Microscope and Electron Backscattered Diffraction to observe reservoir microstructures. The Sultani shale is formed by various types of minerals, the majority composition is 67.25% calcite and 18.38% quartz, with little apatite, dolomite, and pyrite. The geochemical test shows that: The Kerogen type is dominated by type II1; the Sultani shale can be burned directly and continuously in the air, due to it contains a large amount of organic matter, TOC average value is 14.82%; the value of equivalent vitrinite reflectance is between 0.55% and 0.67%. The Sultani shale is high-quality source rock but with low maturity. Based on Optical Microscope and EBSD result, the micrite (calcite grain size<0.004mm) constitute Sultani shale. Normally, the reservoir should have extremely low porosity, but there is an amount of foraminifer shell fossil which forms the pore structure. The remarkable thing is that the fossil pore have large pore volume and it is poorly connected to its neighbor, the hydrocarbon reserve in the isolated pores. The Sultani shale is tight reservoir (large pore volume, but poorly connection) with economically attractive resource potential. However, there will be difficult for exploitation, due to it’s specially pore structure. Acid fracturing is feasible technology to connect the isolated fossil pore, thus significantly increase oil production. The Sultani shale can also be burned directly for power generation and get the lime product at the same time, Surface mining is also feasible exploitation patterns.
Abstract Application of horizontal drilling and hydraulic fracturing technique has made development of shale gas reservoir successful in the United States during the past decade. Chasing its operational success, researchers have been studying to understand the fundamentals of shale gas production, which will provide valuable information to assist in optimization of shale reservoir development. Unfortunately, the mechanism of shale gas production has not been fully revealed so far, and most reservoir simulation models are adopting the mechanism of coalbed methane production to forecast shale gas development process, which might not be the real case. In this paper, instead of using numerical simulation model, artificial intelligence and data mining techniques are implemented to study the controlling factors of shale gas production and understand the impacts of reservoir, completion and stimulation parameters in a dynamic manner only according to the field data. A database of Marcellus shale reservoir is generated by integrating information such as well locations, well trajectories, reservoir characteristics, completion, hydraulic fracturing, and production parameters, etc. Neural network models are trained to learn the key performance impacting factors on shale gas production in a dynamic manner, which could assist reservoir management decisions.
Abstract This paper investigates the applicability of Low Salinity (LoSal) EOR for a Kuwaiti reservoir. Many reservoirs in the Middle East are not producing satisfied results after depletion methods for a long time of production. Therefore, new management and production strategies must be determined in order to meet the global market demand for oil, which can be done using Enhanced Oil Recovery (EOR) techniques. In Kuwait, one of the EOR methods that could be applied is the use of Low-Salinity (LoSal) Water Flood. Results from previous research have clearly shown that LoSal water injection has a significant impact on oil recovery. Although there are many LoSal experimental results reported in the literature, the process mechanisms and the prediction modeling are yet to be fully investigated and understood. As a result, further experimental work is needed in order to be able to develop reliable prediction tools. The research in this paper is an integrated study combining laboratory work to assess the performance of LoSal water flood using live crude, reservoir brine and native core with wettability conditions restored. The core flooding phase will conduct series of low salinity water flood experiments, design of Salt type and concentration. The performance of LoSal will be compared to different salinities water flood based on reservoir water salinity.
Abstract Descriptive Analytics is the first step of a three-step data-driven analytics workflow used for managing and optimizing completion, production and recovery of shale wells. The comprehensive data-driven analytics workflow for the unconventional resources is called Shale Analytics (Mohaghegh 2017). The key behind Shale Analytics is the incorporation of all field measurements that contribute to the productivity of shale wells. There are workflows in the market that claim to be data analytics related but do not make use of all the available field measurements when performing their analyses. These workflows are mainly based on traditional statistical algorithms rather than Artificial Intelligence and Machine Learning. Such approaches represent different versions of Decline Curve Analysis. Shale Descriptive Analytics takes into account seven categories of field measurements; (i) well construction and trajectory, (ii) well spacing and stacking, (iii) reservoir characteristics, (iv) completion design, (v) hydraulic fracturing implementation, (vi) operational constraints, and (vii) well productivity. Each of the above categories of field measurements include several parameters. Shale Descriptive Analytics provides two types of insight on the contribution of all the field measurements to well productivity. The first type of insight compares and quantifies the contribution of the different categories of field measurements to well productivity. The second, more detail type of insight compares and quantifies the contribution of each of the parameters of the first six categories to the final category that is well productivity and then compares all the parameters to one another. The Shale Descriptive Analytics presented in this article demonstrate the results of more than 800 shale wells in one of the most productive shale plays in Texas. Two conclusions have been achieved as the result of this study. (a) In the early life of a shale asset, when the wells are NOT too close to one another (when Frac-Hit is not an issue), using well productivity indices (such as initial production, initial decline rate, first 30, 60, 90, 120, 180 and 365 days of cumulative production, etc.) can provide realistic insight for completion optimization, well productivity and recovery. (b) Once the number of wells in a given asset increases, resulting in the reduction of the distances between parent and child wells (Frac-Hit impacts production and recovery), well productivity indices will no longer be able to provide the required insight for modeling and analysis of field measurements. This is because as the number of wells increases in a given shale asset, the fracture-driven interaction between wells (also known as Frac-Hit) takes over the overall productivity of all the wells in the field. Frac-Hit not only negatively influences parent and child wells productivity and recovery, it completely undermines all the existing techniques (traditional techniques such as RTA and numerical simulation as well as all the existing techniques based on Data Analytics) for completion and production optimization of shale wells. At the conclusion of this article, a new approach to overcome this specific problem is introduced.
The electricity in Indonesia still dominated by fossil energy which might cause many losses, especially to the environment. Renewable energy such as geothermal is needed to replace the fossil energy. It is the right choice in today's global sustainable development due to low carbon emissions. Indonesia has 40% of all geothermal potential all over the world. Geothermal energy potential in Indonesia is widespread in areas that traversed by the ring of fire or areas with active volcanic activity, one of them is Mount Tangkuban Perahu in West Java. Geothermal potential in an area could be known by the anomalies on surface manifestations such as fumaroles, hot spring, mud pools, steaming ground, sinter, and hydrothermal alteration. It is possible to identify areas that have the potential of geothermal energy based on their surface relief through remote sensing. Identification of anomalies obtains through the processing of Landsat 8 satellite imagery using thermal bands 10 and 11 with Thermal Infrared (TIR) sensors. Remote sensing is very effective in identifying manifestations and potential of geothermal energy because it can cover data on a wide area, time, and cost-efficiency. In addition to fluid manifestations, remote sensing can also identify the distribution of minerals in areas to estimate the reservoir characteristics of a geothermal system. This study aims to estimate the geothermal reservoir characteristics in Mount Tangkuban Perahu area that used as an initial consideration in geothermal exploration and further geothermal research in other areas. The methods use in this research are Split-Window Algorithm (SWA) to find the distribution of manifestations, calculations of Radiative Heat Flux (RHF) to obtain an estimation of geothermal resource potential, and bands ratio to identify the distribution of the hydrothermal alteration minerals. The results will provide information about surface temperature and heat losses based on anomalous manifestations, estimation of power electricity resource, surface mineral distribution, geomorphological features, and a conceptual model of Mount Tangkuban Perahu geothermal system.