|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
Abstract The application of high viscosity friction reducers (HVFRs) in unconventional plays has steadily increased over the past years, not only as alternatives to conventional friction reducers (FRs) but also as a direct replacement for the use of guar-based fluids. HVFRs demonstrate more efficient proppant transport, due to their unique rheological properties, concurrently with a high friction reduction effect allowing higher pumping rates. However, all these benefits come with few critical limitations related to frac water quality, compatibility with other additives, and static proppant suspension, which makes them very similar to conventional crosslinked gels regarding their Quality Assurance and Quality Control (QAQC) requirements at a well location during the field implementation. This paper illustrates the comprehensive laboratory efforts undertaken to evaluate different HVFR and crosslinked gel products, their successful field application supported by a robust and effective field QAQC process, and the critical importance of maintaining effective field-laboratory-field interaction/cycle to optimize the fluid design and maximize the results. Experimental studies on different products were conducted to measure the effect of frac water quality, HVFR loading, breaker loading, and compatibility with other additives used in the fluid recipe such as surfactants, scale inhibitors, and biocides. The ability of HVFR to suspend and transport proppant is not only a function of polymer loading but also highly influenced by fluid velocity as static and semi-dynamic proppant suspension tests demonstrate. Additionally, a full dynamic proppant transport test was also conducted using a multi-branched slot apparatus to simulate the flow inside a complex fracture network. Field execution followed a strict QAQC protocol including water analysis, field laboratory tests, water filtration, mixing procedure, product storage, and transport allowing direct onsite replication of the results that had been previously obtained in the laboratory. Constant communication between the field and the laboratory allowed a successful execution of several treatments in a challenging shale play in the Sichuan Region, China. These treatments achieved record proppant placements and, just as importantly, they demonstrated repeatability and consistency over time; which had not previously been attained. Laboratory testing proved critical in confirming that product segregation was occurring, even if there was no visual observation of this phenomenon, which had resulted in initial difficulties in fluid quality and reliability. The presence of constant QAQC engineering support on location was instrumental in rapidly identifying the potential root cause(s) and efficiently and correctly applying the necessary corrective actions. This paper will highlight the importance of laboratory testing, in order to design and optimize the fluid system. The paper will also demonstrate how critical the onsite QAQC is through actual examples of fluid optimization and field implementation. These two activities, although requiring a substantial resource commitment and effort, are both required to achieve successful execution.
The last year has seen people in many sectors unexpectedly confronting a new challenge--working remotely. Even before this, our industry has been trying to operate fields remotely (either partially or fully) and make operations smarter and more automated. Key drivers are to improve safety in operations, maximize production, and make operations more efficient. These efforts have been enabled by the rapidly changing technology landscape--in sophisticated acquisition and analysis of data and increased connectivity (from both fiber-optic and cellular networks). It also has been accelerated by the push across the industry to digitalize.
Africa is on track to becoming the world's most populous region by 2023 as growth in the continent's population surpasses that of China and India; between 2020 and 2040, one in every two births will be African, according to the International Energy Agency (IEA). The problem--and the opportunity--is that three-quarters of those new global citizens living in sub-Saharan Africa will live without access to electricity and other energy-driven staples of the modern world. "More than half a billion people [will be] added to Africa's urban population by 2040, much higher than the growth seen in China's urban population in the two decades of China's economic and energy boom," IEA noted in its Africa Energy Outlook 2019. "Growing urban populations mean rapid growth in energy demand for industrial production, cooling, and mobility," IEA analysts wrote. "The projected growth in oil demand is higher than that of China and second only to that of India as the size of the car fleet more than doubles (the bulk of which have low fuel efficiency) and liquefied petroleum gas (LPG) is increasingly used for clean cooking." With regards to gas, Africa is on track to becoming the third-largest region to feed the growth in global gas demand over the next 20 years, the IEA said (Figure 1).
Israel's Delek Drilling is selling its 22% nonoperated stake in the Tamar gas field offshore Israel to Abu Dhabi's Mubadala Petroleum for up to $1.1 billion in what would be, if finalized, the largest commercial agreement since Israel and the UAE signed the Abraham Accords Peace Agreement in August 2020. Delek announced Tuesday it had signed a nonbinding memorandum of understanding (MOU) with Mubadala Petroleum, a wholly owned subsidiary of the Abu Dhabi government-owned Mubadala Investment Co. Delek said Mubadala would pay up to $1.1 billion for the stake, Delek CEO Yossi Abu said the sale has "the potential to be another major development in our ongoing vision for natural gas commercial strategic alignment in the Middle East, whereby natural gas becomes a source of collaboration in the region. "We are proud to have signed this MOU following the Abraham Accords Peace Agreement between Israel and the UAE," Abu said, adding that he "would like to thank my counterparty at Mubadala Petroleum and our clients in Israel, Egypt, and Jordan." Mubadala Petroleum manages upstream oil and gas exploration and production assets in10 countries, with a primary geographic focus on the Middle East and North Africa, Russia, and Southeast Asia. Adding the Tamar gas field to its portfolio will only build on that as gas production ramps up in the Eastern Mediterranean. US major Chevron became the operator of Tamar when it finalized the purchase of Noble Energy in the autumn of 2020 and so acquired Noble's 25% interest in the field. Net daily production in 2020 from the Tamar field averaged 173 MMcf/D of gas (51 MMcf/D attributed to Chevron in 2020), according to Chevron's 2020 Annual Report Supplement. In the report, Chevron noted: "Progress continues on the Tamar SW development, which consists of one well tied back to Tamar.
Iraq may turn to China to meet its deadline of finalizing the sale of ExxonMobil's 32.7% stake in the West Qurna-1 oil field by the end of June, after Chevron declined to buy out its rival's position. Earlier this month, Iraq's oil ministry had signaled it preferred a US company to replace ExxonMobil as operator of the field near Basra. But when Chevron declined the offer, state-run Basra Oil Co. (BOC) widened its net to consider buyers from outside of the US, BOC Director General Khalid Hamza told Reuters in an interview. "We have no objection either on PetroChina nor CNOOC, they are our partners already," Hamza told Reuters, adding that also "BOC may buy Exxon's share, or any of the oil ministry's companies may buy." ExxonMobil submitted a request in January to sell its stake in West Qurna-1 so as to shed some of the more than $70 billion in debt it accumulated in 2020; debt that resulted in two downgrades by Moody's Investors Service in less than a year, according to Reuters.
Shareholders of Russia's second-largest gas producer, Novatek, have approved $11 billion in external financing for the Arctic LNG 2 project on which Novatek has pledged its 60% equity stake in the project as collateral. The approval came 23 April at the company's annual shareholders' meeting. In making the announcement, Novatek CEO Leonid Mikhelson said that responsibility for fundraising will be split three ways between Russia, China, and the tandem of Japan and Europe acting together. The $21-billion project, which received final investment approval in 2019, is expected to launch production in 2023 as Novatek expands its LNG exports east and west along Russia's now navigable Arctic coast. Arctic LNG 2 will reach full capacity of almost 20 mtpa in 2026, according to the company.
Soroush, Mohammad (RGL Reservoir Management, University of Alberta) | Mohammadtabar, Mohammad (RGL Reservoir Management, University of Alberta) | Roostaei, Morteza (RGL Reservoir Management) | Hosseini, Seyed Abolhassan (RGL Reservoir Management, University of Alberta) | Mahmoudi, Mahdi (RGL Reservoir Management) | Keough, Daniel (Precise Downhole Services Ltd) | Cheng, Li (University of Alberta) | Moez, Kambiz (University of Alberta) | Fattahpour, Vahidoddin (RGL Reservoir Management)
Abstract Distributed Temperature Sensing (DTS) system using optical fiber has been deployed for downhole monitoring over two-decades. Several technological advancements led to a wide acceptance of this technology as a reliable surveillance technique. This paper presents a comprehensive technical review of all the applications of the DTS, with focus on oil and gas industrial deployments. The paper starts with the advantages of the DTS over other methods and an overview of the DTS basics, including theory, the DTS components, deployment types, fiber types, design and limitations. Then, it is followed by the oil and gas applications of the DTS including hydraulic fracturing (during and after fracturing), well treatment/stimulation (acid injection, fluid distribution, diversion monitoring), inorganic (scaling) and organic (wax/asphaltene/hydrate) deposition detection, leak detection (in well and pipeline), flow monitoring (rate monitoring, water/steam injection and SAGD monitoring, CO2 storage monitoring, zonal contribution determination, gas lift optimization) and reservoir/fluid characterization (facies, porosity, permeability and fluid composition determination). This study reviews the historical development, applications and limitations of the DTS systems. The paper mainly focusses on deployment techniques, the application of the DTS for the prediction and surveillance of the non-thermal and thermal producer/injector wells, hydraulically fractured wells and those wells with treatments. The paper provides a concise review using several field cases from over two hundred published papers of Society of Petroleum Engineering (SPE) and journal databases. The application of the DTS in downhole monitoring can be divided into the qualitative and quantitative applications. In quantitative approaches, numerical models should be combined with the DTS data. This study discusses case by case worldwide field applications of DTS along with proposed modeling methods and interpretations. It also summarizes main challenges, including the fiber reliability, longevity, and operational limitations such as the installation and the complexity of quantitative approaches. This study is the foundation for an ongoing study on wellbore and reservoir surveillance through real-time distributed fiber optic sensing recordings along the wellbore. It summarizes the historical development and limitations to identify the existing gaps and reviews the lessons learned through the two decades of the application of the DTS in production performance.
Alkinani, Husam (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology)
Abstract One of the most vital reservoir properties is permeability. It is usually measured using core samples with two major measurement methods; using gas or using liquid. The purpose of this work is to use a data-driven recurrent neural network model to estimate the equivalent liquid permeability based on gas permeability. By using this model, the equivalent liquid permeability can be predicted for the permeability of core samples with rich clay minerals measured using gas (or any core sample that is measured using gas). This will give an alternative way to the currently used method (Klinkenberg method). Core sample data measurements of more than 500 cores were obtained from limestone formations. The data went through a processing step to eliminate any measurement errors. Then, the data were clustered into training, validation, and testing. After many iterations, a decision was made to have a network with four hidden layer and twenty neurons in each hidden layer, and four delays in the input and the output. The findings showed that the network had stopped training after nine epochs with a validation mean squared error (MSE) of 5.3. The model exhibited excellent performance during training, validation, and testing with an overall R2 of 0.91 which is excellent. These findings prove that the model can closely track the actual equivalent liquid permeability measurements using the gas permeability measurements data within a reasonable margin of error. With the rise of machine learning and other artificial intelligence (AI) methods as well as the potential application in the petroleum industry, these methodologies can revolutionize the industry and save time and money.
Abstract Low salinity waterflooding has been an area of great interest for researchers for almost over three decades for its perceived "simplicity," cost-effectiveness, and the potential benefits it offers over the other enhanced oil recovery (EOR) techniques. There have been numerous laboratory studies to study the effect of injection water salinity on oil recovery, but there are only a few cases reported worldwide where low salinity water flooding (LSW) has been implemented on a field scale. In this paper, we have summarized the results of our analyses for some of those successful field cases for both sandstone and carbonate reservoirs. Most field cases of LSW worldwide are in sandstone reservoirs. Although there have been a lot of experimental studies on the effect of water salinity on recovery in carbonate reservoirs, only a few cases of field-scale implementation have been reported for the LSW in carbonate reservoirs. The incremental improvement expected from the LSW depends on various factors like the brine composition (injection and formation water), oil composition, pressure, temperature, and rock mineralogy. Therefore, all these factors should be considered, together with some specially designed fit-for-purpose experimental studies need to be performed before implementing the LSW on a field scale. The evidence of the positive effect of LSW at the field scale has mostly been observed from near well-bore well tests and inter-well tests. However, there are a few cases such Powder River Basin in the USA and Bastrykskoye field in Russia, where the operators had unintentionally injected less saline water in the past and were pleasantly surprised when the analyses of the historical data seemed to attribute the enhanced oil recovery due to the lower salinity of the injected water. We have critically analyzed all the major field cases of LSW. Our paper highlights some of the key factors that worked well in the field, which showed a positive impact of LSW and a comparative assessment of the incremental recovery realized from the reservoir visa-a-vis the expectations generated from the laboratory-based experimental studies. It is envisaged that such a comparison could be more meaningful and reliable. Also, it identifies the likely uncertainties (and their sources) associated during the field implementation of LSW.