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Johnson, Andrew C. (Schlumberger) | Miles, Jeffrey (Schlumberger) | Mosse, Laurent (Schlumberger) | Laronga, Robert (Schlumberger) | Lujan, Violeta (Schlumberger) | Aryal, Niranjan (Schlumberger) | Nwosu, Dozie (Schlumberger)
Abstract Formation water saturation is a critical target property for any comprehensive well log analysis program. Most techniques for computing saturation depend heavily on an analyst’s ability to accurately model resistivity measurements for the effects of formation water resistivity and rock texture. However, the pre-requisite knowledge of formation water properties, particularly salinity, is often either unknown, varying with depth or lateral extent, or is difficult to derive from traditional methods. A high degree of variability may be present due to fluid migration from production, water injection, or various geological mechanisms. In unconventional reservoirs, the complexity of the rocks and pore structure further complicates traditional interpretation of the available well logs. These factors introduce significant uncertainties in the computed fluid saturations and therefore can substantially affect final reserves estimates. A novel technique in geochemical spectroscopy has recently been introduced to distinguish the chlorine signals of the formation and borehole. The new, quantitative measurement of formation chlorine enables a direct calculation of bulk water volume for a given formation water salinity. When integrated into a multi-physics log analysis workflow, the chlorine-derived water volume can provide critical information on fluid saturations, hydrocarbon-in-place, and producibility indicators. This additional information is especially useful for characterizing challenging and complex unconventional reservoirs. We present the new technique through several full petrophysical evaluation case studies in organic shale formations across the U.S., including the Midland, Delaware, Marcellus, and DJ basins. We solve for formation-specific water salinity and bulk water volume through an optimization that combines chlorine concentration with resistivity and dielectric measurements. These outputs are integrated into comprehensive petrophysical evaluations, leveraging a suite of advanced well log measurements to compute final fluid and rock properties and volumetrics. The evaluations include geochemical mineralogy logs, 2D NMR analyses, dielectric dispersion analyses, basic log measurements, and multi-mineral models. The results underscore the utility of the new spectroscopy chlorine log to reduce petrophysical model uncertainties in an integrated workflow. While this workflow has been demonstrated here in several U.S. organic shale case studies, the fundamental challenges it addresses will make it a valuable solution for a range of unconventional reservoirs globally.
Swiss oil trader Vitol said on 30 April that its oil and gas subsidiary, Vencer Energy, was buying Hunt Oil Company's assets in the Permian Basin for an undisclosed sum. Media outlets including Bloomberg and Reuters cited sources that pegged the asking price at around $1 billion. Houston-based Vencer was established last year as the trading giant's first foray into the upstream sector. The assets include leases on 44,000 acres in the Midland Basin side of the Permian, with an output about 40,000 BOE/D. "This is an important day for Vencer as it establishes itself as a significant shale producer in the US Lower 48. We expect US oil to be an important part of global energy balances for years to come, and we believe this is an opportune time for investment into an entry platform in the Americas," said Ben Marshall, the head of Vitol's Americas business unit.
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
Abstract This paper explores a holistic approach to characterize trouble stages by applying automated event recognition of abnormal pressure increases and associating those events to formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable both in the short-term (reducing wasted fluid and chemicals, and other remediation measures) and in the long-term (increased well productivity). Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. In this study, the identification of problematic frac stages was initially performed manually (stage-by-stage) using a cloud-based hydraulic fracture data application. During this process, the team recognized that the problem stages had their own characteristic pressure signature - a sudden unexplained pressure increase in the absence of rate changes. A machine learning algorithm was then developed to automatically identify this type of signature. Additionally, previously published machine learning algorithms were used to recognize other operational events of interest, e.g., when proppant reaches the perforations. Then by combining the various events and creating short search windows around each abnormal pressure increase, it is possible to find concurrent operations that may be associated with the observed pressure behavior. A subsequent statistical analysis revealed that abnormal pressure increases often coincided with changes in proppant concentration in problem stages (stages with abnormal treating pressure behavior). This behavior may be due to near-wellbore effects caused by the changing fluid flow dynamics. Furthermore, it was observed that treating pressures that behaved contrary to hydrostatic pressure effects may be useful in identifying when injectivity is lost and provide an early signal for screen outs. Through this holistic approach, we were able to identify trouble stages and discern some diagnostics for automated detection of abnormal treating pressure increases. The team was able to identify areas within the stages that were inefficiently pumped, resulting in cost-savings through optimization of proppant and friction reducer (FR) loadings while maintaining a level of caution to prevent screen outs. Finally, the automated detection of pressure anomalies offers a pathway to the real-time prediction and avoidance of operational difficulties such as pressure outs and screen outs.
Chen, Chi (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Shouxin (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lu, Cong (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Kun (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lai, Jie (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Liu, Yuxuan (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University)
Abstract Hydraulic fracturing technology provides a guarantee for effective production increase and economic exploitation of shale gas wells reservoirs. Propped fractures formed in the formation after fracturing are the key channels for shale gas production. Accurate evaluation of local propped fracture conductivity is of great significance to the effective development of shale gas. Due to the complex lithology and well-developed bedding of shale, the fracture surface morphology after fracturing is rougher than that of sandstone. This roughness will affect the placement of the proppant in the fracture and thus affect the conductivity. At present, fracture conductivity tests in laboratories are generally based on the standard/modified API/ISO method, ignoring the influence of fracture surface roughness. The inability to obtain the rock samples with the same rough morphology to carry out conductivity testing has always been a predicament in the experimental study on propped fracture conductivity. Herein, we propose a new method to reproduce the original fracture surface, and conductivity test samples with uniform surface morphology, consistent mechanical properties were produced. Then, we have carried out experimental research on shale-propped fracture conductivity. The results show that the fracture surfaces produced by the new method are basically the same as the original fracture surfaces, which fully meet the requirements of the conductivity test. The propped fracture conductivity is affected by proppant properties and fracture surface, especially at low proppant concentration. And increasing proppant concentration will help increase the predictability of conductivity. Due to the influence of the roughness of the fracture surface, there may be an optimal proppant concentration under a certain closure pressure.
Wu, Tao (CNPC Chuanqing Drilling Engineering Co.LTD) | Fang, Hanzhi (Yangtze University) | Sun, Hu (CNPC Chuanqing Drilling Engineering Co.LTD) | Zhang, Feifei (Yangtze University) | Wang, Xi (Yangtze University) | Wang, Yidi (Yangtze University) | Li, Siyang (Yangtze University)
Abstract Unconventional reservoirs such as shale and tight sandstones that with ultra-low permeability, are becoming increasingly significant in global energy structures (Pejman T, et al., 2017). For these reservoirs, successful hydraulic fracturing is the key to extract the hydrocarbon resources efficiently and economically. However, the intrinsic mechanisms of fracturing growth in the tight formations are still unclear. In practice, fracturing design mainly depends on hypothetical models and previous experience, which leads to difficulties in evaluating the performance of the fracturing jobs. Therefore, an improved method to optimize parameters for fracturing is necessary and beneficial to the industry. In this paper, a data-driven approach is used to evaluate the factors that dominate the production rate from tight sandstone formation in Changqing Field which is the largest oil field in China. In the model, the input parameters are classified into two categories: controllable parameters (e.g. stage numbers, fracturing fluid volume) and uncontrollable parameters (e.g. formation properties), and the output parameter is the accumulated oil production of the wells. Data for more than 100 wells from different formations and zones in Changqing Field are collected for this study. First, a stepwise data mining method is used to identify the correlations between the target parameter and all the available input parameters. Then, a machine learning model is developed to predict the well productivity for a given set of input parameters accurately. The model is validated by using separate data-sets from the same field. An optimize algorithm is combined with the data-driven model to maximize the cumulative oil production for wells by tuning the controllable parameters, which provides the optimized fracturing design. By using the developed model, low productivity wells are identified and new fracturing designs are recommended to improve the well productivity. This paper is useful for understanding the effects of designed fracturing parameters on well productivity in Changqing Oilfield. Furthermore, it can be extended to other unconventional oil fields by training the model with according data sets. The method helps operators to select more effective parameters for fracturing design, and therefore reduce the operation costs for fracturing and improve the oil and gas production.
Pilot Water Solutions has expanded its position as a major disposal player in the Delaware Basin of west Texas with its acquisition of Felix Water. Financial terms of the deal were not disclosed. Pilot Water is a majority-owned subsidiary of Pilot Company, a supplier of gasoline and the largest operator of travel centers in North America. The addition of the Felix assets will bring Pilot Water's disposal-well count in the Delaware to 23. The combined company will also boast 210 miles of produced water pipeline and over 500,000 bbl of water per day of disposal capacity in the region.
Equinor has agreed to sell its Bakken Shale operation for $900 million, ending a decade long struggle to make money in the US shale oil business. The buyer, Grayson Mill Energy, is acquiring wells producing around 48,000 BOE/D and 242,000 operated and non-operated acres in North Dakota and Montana. The Norwegian oil company's remaining shale holdings are in the gas producing Marcellus and Utica Shale formations in the eastern US, which it has been paring in recent years. "We are taking action to improve the profitability of Equinor's international oil and gas business," said Al Cook, executive vice president of development and production international at Equinor. He added that Grayson Mill agreed to hire Equinor's Bakken field team and a "significant number of the support teams."
Chesapeake Energy, the once high-flying player in the US shale revolution, has completed its restructuring and emerged from Chapter 11 bankruptcy, equitizing about $7.8 billion of debt under a court-approved plan. As of February 9, 2021, Chesapeake's principal amount of debt outstanding was $1.27 billion, compared to roughly $9.1 billion as of June 30, 2020. "We have fundamentally reset our business, and with an improved capital and cost structure, disciplined approach to capital reinvestment, diverse asset base and talented employees, we are poised to deliver sustainable free cash flow for years to come.," said Chesapeake president and chief executive Doug Lawler. Upon emergence, the Oklahoma City-based company entered into a credit facility with a $2.5 billion borrowing base, with amounts maturing in 2024 and 2025. Chesapeake had approximately $50 million borrowed on the facility at February 9, 2021, as well as $51 million reserved for undrawn letters of credit outstanding.
Summary Recent studies have indicated that huff ‘n’ puff (HNP) gas injection has the potential to recover an additional 30 to 70% oil from multifractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution) and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example. Compositional simulations are conducted that incorporate a tuned pressure/volume/temperature (PVT) model and a set of measured cyclic injection/compaction pressure‐sensitive permeability data. Markov‐Chain Monte Carlo (MCMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The MCMC process is accelerated by using an accurate proxy model (kriging) that is updated using a highly adaptive sampling algorithm. Gaussian processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ‐σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions. The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half‐length, are narrower, whereas wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of approximately 1.5 months, a production time of approximately 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubblepoint are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production. The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced techniques for optimization under uncertainty, resulting in better decision making.