The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Khan, Mohammad Rasheed (SLB) | Kalam, Shams (King Fahd University of Petroleum & Minerals) | Asad, Abdul (SPRINT Oil & Gas services) | A. Abu-khamsin, Sidqi (King Fahd University of Petroleum & Minerals)
Abstract Unconventional reservoirs like shale oil/gas are expected to play a major role in many unexplored regions, globally. Shale resource evaluation involves the estimation of Total Organic Carbon (TOC) which correlates to the prospective capability of generating and containing hydrocarbons. Direct measurement of TOC through geochemical analysis is often not feasible, and hence researchers have focused on indirect methods to estimate TOC using analytical and statistical techniques. Accordingly, this work proposes the application of artificial intelligence (AI) techniques to leverage routinely available well logs for the prediction of TOC. Multiple algorithms are developed and compared to rank the most optimum solution based on efficiency analysis. Support Vector Regression (SVR), Random Forest (RF), and XGBoost algorithms are utilized to analyze the well-log data and develop intelligent models for shale TOC. A process-based approach is followed starting with systematic data analysis, which includes the selection of the most relevant input parameters, data cleaning, filtering, and data-dressing, to ensure optimized inputs into the AI models. The data utilized in this work is from major shale basins in Asia and North America. The AI models are then used to develop TOC predictor as a function of fundamental open-hole logs including sonic, gamma-ray, resistivity, and density. Furthermore, to strengthen AI input-output correlation mapping, a k-fold cross-validation methodology integrating with the exhaustive-grid search approach is adopted. This ensures the optimized hyperparameters of the intelligent algorithms developed in this work are selected. Finally, developed models are compared to geochemically derived TOC using a comprehensive error analysis schema. The proposed models are teted for veracity by applying them on blind dataset. An error metrics schema composed of root-mean-squared-error, and coefficient of determination, is developed. This analysis ranks the respective AI models based on the highest performance efficiency and lowest prediction error. Consequently, it is concluded that the XGBoost and SVR-based TOC predictions are inaccurate yielding high deviations from the actual measured values in predictive mode. On the other hand, Random Forest TOC predictor optimized using k-fold validation produces high R values of more than 0.85 and reasonably low errors when compared to true values. The RF method overpowers other models by mapping complex non-linear interactions between TOC and various well logs.
Abstract This paper presents the results of numerical simulations of hydraulic fracturing in the immediate vicinity of the wellbore. This research aims to identify the primary mechanisms underlying the complexities in both the fracture morphology and propagation of longitudinal fractures. The study shows that the perforation attributes and characteristics, the cement quality, and the reservoir heterogeneity have a significant impact on the resulting morphology and the trajectory of the propagating hydraulic fracture. The study is based on properties and conditions associated with a field study conducted in the Austin Chalk formation, and concludes that the pattern and the dimensions of the perforations are essential factors controlling the fracture initiation pressure and morphology. The results of the simulation studies provide insights into the principles and mechanisms controlling fracture branching and the initiation of longitudinal fractures in the near-wellbore region and can lead to improved operational designs for more effective fracturing treatments.
Khosravi, Maryam (Technical University of Denmark) | Xu, Yao (Technical University of Denmark) | Mirazimi, Seyedamir (Technical University of Denmark) | Stenby, Erling Halfdan (Technical University of Denmark) | Yan, Wei (Technical University of Denmark)
Abstract Carbon sequestration in depleted reservoirs or aquifers is highly demanded but still faced with technical challenges in many aspects. Among them, losing well injectivity during the storage process is a major concern. This can be caused by salt deposited in the reservoir, particularly near the injection well, which may sometimes creep into the injection well. Therefore, it is desirable to estimate the amount and distribution of salt precipitation at the injection conditions for a smooth implementation of CO2 sequestration. In this paper, we investigate how much commercial software CMG-GEM can help the evaluation of salt precipitation. We first review the critical mechanisms involved in salt precipitation and then analyze the challenges in simulating these mechanisms. According to the literature, water saturation and saturation index are the two most influential parameters that control the amount and pattern of salt precipitation and clogging due to water vaporization. Their values are determined by the complex interplay between viscous force, gravity, the evaporation of water into the CO2 stream, the molecular diffusion of dissolved salt in the brine, and surface phenomena such as the spreading of a thin water film on the rock surface, the Marangoni convection, and disjoining suction. Here we investigate the challenges of simulating the aforementioned mechanisms as well as salt precipitation due to the backflow of brine toward the injection well. The surface-related phenomena are difficult to account for in simulation. However, the extent of the CO2 plume can be significantly underestimated if they are neglected. Although water vaporization, salt diffusion, and capillary pressure can be formally included in the simulation, it is arguable whether they always describe the actual phenomena adequately. In most cases of CO2 injection into an aquifer, water spreads all over the rock surface, which increases the rate of vaporization and surface-related phenomena, such as the Marangoni effect, dramatically. Marangoni turbulent fluxes originating from the unbalanced shear stresses on the interface can accelerate the mixing effect in homogenizing the ions composition, which results in self-enhanced salt precipitation via the thin brine film spreading on the rock surface. We examine different simulation techniques as remedies to mimic those phenomena.
While the M&A spotlight continues to shine on the prolific Permian Basin sprawled across western Texas and southeastern New Mexico, the Williston Basin of Montana and the Dakotas shimmers with consolidation opportunities as shale producers look to add more reserves to their inventory. US-independent Chord Energy recently announced the acquisition of about 62,000 net acres in the Williston Basin from XTO Energy for total cash consideration of 375 million. "The acquired assets are an excellent strategic and operational fit to Chord's premier Williston Basin acreage position," said Danny Brown, Chord's president and chief executive. "These low-cost, tier-one assets are highly competitive with our existing portfolio and further extend our inventory runway. Consolidation in the core of the basin supports longer laterals, higher capital, and operating efficiencies, strong financial returns, and sustainable free cash flow generation."
Wu, Bohong (Research Institute of Petroleum Exploration & Development, PetroChina) | Nie, Zhen (Research Institute of Petroleum Exploration & Development, PetroChina) | Li, Yong (Research Institute of Petroleum Exploration & Development, PetroChina) | Deng, Xili (Research Institute of Petroleum Exploration & Development, PetroChina) | Ma, Ruicheng (Research Institute of Petroleum Exploration & Development, PetroChina) | Xu, Jiacheng (Research Institute of Petroleum Exploration & Development, PetroChina)
Abstract Marginal reserves are an important play in future energy development. Based on the statistics of China National Petroleum Corporation (CNPC), the low permeability and unconventional reservoirs occupied 92% of newly found proven reserves in China. To overcome challenges such as poor reservoir conditions, weak natural energy, low displacement efficiency, and insufficient single well production, CNPC has conducted years of research and operation to cost-effectively develop China's marginal reserves. To develop the marginal fields economically, it is required to maximize single well production, recovery and reservoir sweep with minimum CAPEX and OPEX reasonably. The production enhancement is realized by 3 key technologies, namely, sweet spot identification, multi-layered 3D short spacing horizontal well pattern, and volumetric fracturing techniques. The cost reduction is achieved by the full life cycle practice of utilizing "large cluster, factory" well design and field operation, drilling prognosis optimization, integrated intelligent surface system, and unmanned operation. CNPC cost-effective development mode is practical and successful, marginal fields characterized with heterogeneous, multi-layered oil-bearing intervals with poor continuity are being economically developed in China. By comprehensive geological study, fit-for-purpose technologies application, and geoscience-to-engineering integration, the fracture control degree of horizontal wells increased from 60% to more than 90% based on micro-seismic events, stimulated reservoir volume (SRV) increased by 46.8%, average cumulative oil production per well is more than 100 times than original production in the field. Fast and early cash flow is realized by minimum production facilities. The average drilling cycle is shortened by 61%, the surface facility construction time is reduced by 65%, and the average single well investment is reduced by 42%.
Zhu, Jun (Vertechs Energy Group) | Zhang, Wei (Vertechs Energy Group) | Zeng, Qijun (Vertechs Energy Group) | Liu, Zhenxing (Vertechs Energy Group) | Liu, Jiayi (PetroChina Southwest Oil & Gas Field Company) | Liu, Junchen (PetroChina Southwest Oil & Gas Field Company) | Zhang, Fengxia (PetroChina Southwest Oil & Gas Field Company) | He, Yu (PetroChina Southwest Oil & Gas Field Company) | Xia, Ruochen (PetroChina Southwest Oil & Gas Field Company)
Abstract In the past decade, the operators and service companies are seeking an integration solution which combines engineering and geology. Since our drilling wells are becoming much more challenging than ever before, it requires the office engineer not only understanding well construction knowledge but also need learn more about geology to help them address the unexpected scenarios may happen to the wells. Then a novel solution should be provided to help engineers understanding their wells better and easier in engineering and geology aspects. The digital twin technology is used to generate a suppositional subsurface world which contains downhole schematic and nearby formation characteristics. This world is described in 3D modelling engineers could read all the information they need after dealt with a unique algorithm engine. In this digital twin subsurface world, the engineering information like well trajectory, casing program, BHA (bottom hole assembly) status, are combined with geology data like formation lithology, layer distribution and coring samples. Both drilling or completion engineers and geologist could get an intuitive awareness of current downhole scenarios and discuss in a more efficient way. The system has been deployed in a major operator in China this year and received lot of valuable feedback from end user. First of all, the system brings solid benefits to operator's supervisors and engineers to help them relate the engineering challenges with according geology information, in this way the judgement and decision are made more reliable and efficiently, also the solution or proposal could be provided more targeted and available. Beyond, the geology information from nearby wells in digital twin modelling could also provide an intuitional navigation or guidance to under-constructed wells avoid any possible tough layers via adjusting drilling parameters. This digital twin system breaks the barrier between well construction engineers and geologists, revealing a fictive downhole world which is based on the knowledge and insight of our industry, providing the engineers necessary information to support their judgement and assumption at very first time when they meet downhole problems. For example, drilling engineers would pay extra attention to control the ROP (rate of penetration) while drilling ahead to fault layer at the first time it is displayed in digital twin system, which prevent potential downhole accident and avoid related NPT (non-production time). The integration of engineering and geology is a must-do task for operators and service companies to improve their performance and reduce downhole risks. Also, it provides an interdisciplinary information to end user for their better awareness and understanding of their downhole asset. Not only help to avoid some possible downhole risks but also benefit on preventing damage reservoir by optimizing the well construction parameters.
Smith, Christopher M (Advanced Hydrocarbon Stratigraphy) | Nolan, Seth (Hilcorp Alaska, LLC) | Edwards, Reid (Hilcorp Alaska, LLC) | Conrad, Caleb (Baker Hughes) | Gordon, Patrick S (Advanced Hydrocarbon Stratigraphy) | Smith, Timothy M (Advanced Hydrocarbon Stratigraphy) | Smith, Michael P (Advanced Hydrocarbon Stratigraphy)
Abstract Hilcorp's Milne Point S-203 was drilled in 2019 targeting the biodegraded heavy oil of the Ugnu Formation, for exploration and development; being one of the first Ugnu wells to be successfully drilled, completed, and conventionally produced. S-203 crossed three fault blocks and intersected multiple Ugnu subunits. A volatiles analysis, via rock volatiles stratigraphy (RVS), of the cuttings from the main borehole and sidetracks enabled a spatial assessment of oil quantity, microbial activity, and the effect of faults in the different subunits. Produced oil from early in the life cycle of the well was analyzed with RVS, both RVS datasets were combined with completions to assess production contribution across the borehole. These results provide important insights for development of the Ugnu as a heavy oil play on the Alaskan North Slope.
Smith, Christopher M (Advanced Hydrocarbon Stratigraphy) | Smith, Michael P (Advanced Hydrocarbon Stratigraphy) | Gordon, Patrick S (Advanced Hydrocarbon Stratigraphy) | Smith, Timothy M (Advanced Hydrocarbon Stratigraphy) | Duncan, Ed (Duncan Petroleum Advisors)
Abstract Coming into 2021 previous work had identified major potential oil targets throughout the Brookian Campanian Section and in the Kuparuk River Sands of Great Bear Pantheon's (GBP) Talitha and Alkaid units and Theta West leasehold on the North Slope of Alaska which sit immediately adjacent to the Dalton Highway and Tans Alaska Pipeline System approximately 20 miles south of the town of Dead Horse in Prudhoe Bay. These identified targets were based on a combination of previous drilling by ARCO with their Pipeline State 1 well (drilled in 1988) and Alkaid 1 drilled by GBP (drilled in 2015 by Great Bear Petroleum at the time) in addition to 3D seismic owned by GBP. Following Great Bear Petroleum's merger in 2019 with Pantheon Resources to become Great Bear Pantheon and a successful test at the Alkaid 1 well an expanded project of exploration and appraisal wells was launched. This project began with the drilling of the Talitha A exploration well and continued with the Theta West 1 and Alkaid 2 wells. All these wells encountered multiple and significant oil accumulations in the Brookian Campanian Section. A key part of these exploration and appraisal wells was the analysis of sealed at well site and unsealed gently airdried drill cuttings by Rock Volatiles Stratigraphy (RVS) (also known as Volatiles Analysis Service (VAS)) developed by Advanced Hydrocarbon Stratigraphy (AHS). RVS enables the direct measurement of the C1-10 hydrocarbons, water, and several other volatile compounds relevant to evaluating petroleum systems via a gentle extraction, identification, and quantification process on a novel cryo-trap mass spectroscopy system developed in house by AHS. The RVS results have been especially helpful in the analysis of the Brookian Campanian Section given the petrophysically challenging nature of the play, representing an independent measurement relating to hydrocarbons (HC) and water that can be paired with the petrophysics to provide greater confidence in the identification of pay zones. RVS analysis and interpretation is done blind with no additional information beforehand significantly diminishing opportunities for bias in the interpretation of the results before being paired with other datasets. Beyond observations about HC and water content, significant additional information about the petroleum system such as oil quality (API gravity and biological activity), rock/reservoir properties, seals/compartments, and overall strength of the system was provided via RVS. Many of these results were used in the planning of subsequent well site activities like perforations and have been proven accurate in resulting flow tests. When combined with other data like 3D seismic the RVS data enables an immense appreciation of the world class asset that the multiple continuous accumulations in the Brookian Campanian Section of GBP's acreage represents.
Abstract Oil production via horizontal wells with multistage fracture stimulation treatment completions in the Bakken shale of North Dakota and Montana began in 2003. Since then, over 19,000 Bakken shale horizontal wells have been completed and placed into production. Oil production from horizontal Bakken shale oil wells peaked in November 2019 at 1.5 million barrels/day, and is at about 1.2 million barrels/day as of September, 2022 (EIA). There have been several shale oil EOR tests conducted over the last several years, involving the injection of water, CO2 and natural gas. This paper builds upon shale EOR modeling work described in a 2019 NETL report. In that report, a compositional simulation model of the Bakken was constructed, and a production history match on primary oil, gas and water production from a group of wells was obtained. The match model was then used to evaluate the enhanced oil recovery via cyclic injection of CO2, dry gas, and wet gas. This paper utilizes some data from that report to assess two novel, proprietary shale oil EOR processes in the Bakken, in the same area of the Williston Basin. The paper illustrates how these proprietary shale oil EOR processes may be implemented at lower BHP to mitigate interwell communication, while enabling greater oil recovery than via injection of water, CO2 or natural gas. Compositional reservoir simulation modeling of the two novel EOR processes in the modeled Bakken shale wells indicates potential incremental oil recoveries of 200% and 300% of primary EUR may be achieved. The two novel shale oil EOR methods utilize a triplex pump to inject a liquid solvent having a specific composition into the shale oil reservoir, and a method to recover the injectant at the surface, for storage and reinjection. One of the processes enables further enhanced oil recovery via cyclic fracture stimulation at the start of the EOR process. The processes are fully integrated with compositional reservoir simulation to optimize the recovery of residual oil during each injection and production cycle. The patent pending shale oil EOR processes have numerous advantages over cyclic gas injection - shorter injection time, longer production time, smaller, lower cost injection volumes, no gas containment issue - much lower risk of interwell communication, elimination of the need to buy and sell injectant during each cycle, much better economics, scalability, faster implementation, optimization via integration with compositional reservoir simulation modeling, and lower emissions. If implemented early in the well life, their application may preclude the need for artificial lift, to produce more oil sooner, resulting in a shallower decline rate and higher reserves.
Cornelio, Jodel (University of Southern California) | Mohd Razak, Syamil (University of Southern California) | Cho, Young (University of Southern California) | Liu, Hui-Hai (Aramco Americas) | Vaidya, Ravimadhav (Aramco Americas) | Jafarpour, Behnam (University of Southern California)
Abstract Given sufficiently extensive data, deep-learning models can effectively predict the behavior of unconventional reservoirs. However, current approaches in building the models do not directly reveal the causal effects of flow behavior, underlying physics, or well-specific correlations; especially when the models are trained using data from multiple wells of a large field. Field observations have indicated that a single reservoir does not have similar production behaviors. This makes pre-filtering the data to build local models that capture region specific correlations more pertinent than a single global model that will provide averaged-out predictions from different correlations. In this work, we investigate a sophisticated network architecture to expedite the clustering process by training the global model. We utilize attention-based (transformer) neural networks for the input data before mapping to the target variable to extract the attention scores between well properties and the production performance. We leverage the interpretability from these attention-based models to improve the prediction performance for data-centric models derived from clustered datasets. We show the benefits of building local models that are more accurate as they learn correlations that are more region/data specific. Specifically, by utilizing the attention mechanism, we can separate and curate data subsets to train local models, improving the prediction performance by reducing the variability in the entire field.