bluuebunny/crossref_metadata_2025_split huggingface dataset:
Co-Development of Multiple Targets in Permian Unconventional Reservoirs
Authors: Cao Richard , Chen Chaohui , Girardi Alejandro , Li Ruijian , Chowdhury Nitin 
Published: September 2019

Abstract
Optimum co-development layout of multiple targets for unconventional reservoirs is extremely challenging due to complex 3-dimentional well interactions, stochastic well performance, complex fracture geometry, dynamic SRV/DRV evolution, heterogeneous rock properties, various operating conditions, and different economic drivers. In this study, an integrated workflow is developed and applied for co-development of multiple targets in Permian unconventional reservoirs.
In this workflow, the field pilot and trial measurements, Microseismic, geochemistry measurement, data analytics, detailed geomechanical and reservoir modeling, stochastic multiple history matching and forecast, all combined to quantify the horizontal and vertical interference factors and obtain production profiles for different co-development designs. The stochastic behavior of the well performance is explored from three different aspects: static rock properties, dynamic fracturing, and production. The SRV/DRV evolution are presented as the probability distribution function of half fracture length from Microseismic data and effective drainage half-length from stochastic modeling.
Production Forecasting: Optimistic and Overconfident – Over and Over Again
Authors: Bratvold Reidar B , Mohus Erlend , Petutschnig David , Bickel Eric 
Published: September 2019

Abstract
The oil & gas industry uses production forecasts to make a number of decisions as mundane as whether to change the choke setting on a well, or as significant as whether to develop a field. As these forecasts are being used to develop cashflow predictions and value and decision metrics such as Net Present Value and Internal Rate of Return, their quality is essential for making good decision. Thus, forecasting skills are important for value creation and we should keep track of whether production forecasts are accurate and free from bias.
In this paper we compare probabilistic production forecasts at the time of the development FID with the actual annual production to assess whether the forecasts are biased; i.e., either optimistic, overconfident, or both.
While biases in time and cost estimates in the exploration & production industry are well documented, probabilistic production forecasts have yet to be the focus of a major study. The main reason for this is that production forecasts for exploration & production development projects are not publicly available. Without access to such estimates, the quality of the forecasts cannot be evaluated.
Drawing on the Norwegian Petroleum Directorates (NPD) extensive database, annual production forecasts, given at time of project sanction (FID), for 56 fields in the 1995 – 2017 period, have been compared with actual annual production from the same fields. The NPD guidelines specify that the operators should report the annual mean and P10/90-percentiles for the projected life of the field at the time of the FID; that is, the forecasts should be probabilistic. The actual annual production from the fields was statistically compared with the forecast to investigate if the forecasts were biased and to assess the financial impact of such biases.
This paper presents the results from the first public study of the quality of probabilistic production forecasts. The main conclusions are that production forecasts that are being used at the FID for E&P development projects are both optimistic and overconfident. As production forecasts form the basis for the main investment decision in the life of a field, biased forecasts will lead to poor decisions and to loss of value.
A Novel Workflow for Oil Production Forecasting using Ensemble-Based Decline Curve Analysis
Authors: Hakim Elahi Siavash 
Published: September 2019

Abstract
In the absence of well-developed calibrated geologic and simulation models, empirical approaches such as decline curve analysis (DCA) are normally used for production forecasting and reserve estimation. DCA is computationally more efficient compared to simulation models when the active well base exceeds hundreds of wells. However, the underlying assumption for conventional DCA is no change in well operation settings. Moreover, the common approach for production forecasting consists of manual outlier detection and removal, interpretation of missing measurements and data fitting using different models for each well. Therefore, the process of conventional DCA is subjective due to the lack of a standard workflow for preprocessing and data cleansing. The common practice for doing DCA has three main steps: 1. Finding the most representative period in the history of well, 2. Detecting the initial rate (start point) of forecast, 3. Selecting the type of decline and fitting the appropriate model to data points. The solutions to these problems could vary from engineer to engineer and it can be time consuming to analyze all wells manually. To address these issues, we developed a novel workflow based on stochastic methods for detecting various well interventions including change in artificial lift, pump changes and acid treatment, and for forecasting oil production rate more accurately in the presence of uncertainty. The novelty of the proposed ensemble-based approach is forecasting conditioned on various well interventions. Furthermore, the proposed unsupervised stochastic anomaly detection method will detect various well works (or events) in the case of missing records of time and type of events. In this paper, we designed two experiments to test the proposed workflow for oil production rate forecasting and evaluation of acid treatments.
A Model Predictive Control Method for Autonomous Directional Drilling
Authors: Demirer Nazli , Zalluhoglu Umut , Marck Julien , Gharib Hossam , Darbe Robert 
Published: September 2019

Abstract
Directional drilling for hydrocarbon exploration has been challenged to become more cost-effective and consistent with fast-growing drilling operations for both offshore and onshore production areas. Autonomous directional drilling provides a solution to these challenges by providing repeatable drilling decisions for accurate well placement, improved borehole quality, and flexibility to adapt smoothly to new technologies for drilling tools and sensors. This work proposes a model predictive control (MPC)-based approach for trajectory tracking in autonomous drilling. Given a well plan, bottomhole assembly (BHA) configuration, and operational drilling parameters, the optimal control problem is formulated to determine steering commands (i.e., tool face and steering ratio) necessary to achieve drilling objectives while satisfying operational constraints.
The proposed control method was recently tested and validated during multiple field trials in various drilling basins on two- and three-dimensional (2D and 3D) well plans for both rotary steerable systems (RSS) and mud motors. Multiple curve sections were drilled successfully with automated steering decisions, generating smooth wellbores and maintaining proximity with the given well plan.
Active Student Engagement in Learning - Using Virtual Reality Technology to Develop Professional Skills for Petroleum Engineering Education
Authors: Retnanto Albertus , Fadlelmula Mohamed , Alyafei Nayef , Sheharyar Ali 
Published: September 2019

Abstract
Virtual reality (VR) technology, which is the most immersive type of reality technology, can be used in simulating real workplace for occupational safety, trainings, and educational purposes. It encourages students to develop deeper understanding of content. In addition, it allows active learning that transitions students from being mere recipients of information to being participants actively engaged in the learning process. The implementation of this technology makes it possible to improve the effectiveness of the education by simplifying what is normally challenging to understand in physical classrooms. For example, VR can be used to help in visualizing and understanding the differences between different offshore oil and gas rigs. Field trips are one way of adding variety to instruction, thus optimizing teaching effctiveness while motivating student learning. Field trips help bridge formal and informal learning by connecting the students to the real-world experiences. Despite research confirming enhanced learning, sometimes it is not feasible to arrange the fild trips due to several logistical and safety reasons. This study set out to bring real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus. The goal is develop a tool that overcome the restrictions that are associated with the real field trips, increase the students’ interest and enhance their learning experience by using innovative teaching method in petroleum engineering courses. The developed VR-based field trips allowed petroleum engineering students to experience a range of field areas at a fraction of the cost of conventional real field trips, whilst forgoing restrictions due harsh climate and limited accessibility in relative comfort, addressing gender and disability equality within engineering education. Besides all this, the VR-based field trips allowed the students to access the reservoir, which is not possible in reality. Finally, the student feedback on the VR-based field trip demonstrate positive support of learning methods as it creates amazing experiences, and they feel it is exciting and engaging. In addition it also provided an effective way of emphasizing the learning process in petroleum engineering education and promotes active learning for new generations.
Data-Analytics-Based Wireline Formation Tester Adaption and Optimization Study, West China Ultradeep Formation
Authors: Guo Qingbin , Qiu Bin , Zhao Yuanliang , Fan Zhaoya , Chen Jichao , Han Yifu , Zhang Tao , Li Kaixuan , Yu Hua , Jiang Lei , Wei Guo , Yu Daiguo 
Published: September 2019

Abstract
The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical.
More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis.
These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.
Innovative Artificial Intelligence Approach in Vaca Muerta Shale Oil Wells for Real Time Optimization
Authors: Romero Quishpe Adriana , Silva Alonso Katherine , Alvarez Claramunt Juan Ignacio , Barros Jose Luis , Bizzotto Pablo , Ferrigno Eugenio , Martinez Gustavo 
Published: September 2019

Abstract
A well is in natural flowing state when its bottom-hole pressure is enough to produce to the surface. Natural flowing well’s production is regulated by using surface restrictions to regulate the production rate in such a way that the overall well performance is a function of several variables. Examples of these variables are tubing size, choke size, wellhead pressure, flow line size, and perforation density. This implies that changing any of these variables will modify well performance. One of the techniques for the analysis of production performsnce is studying the wellhead pressure declination, since, in critical flow conditions, flow is a function of wellhead pressure. From wellhead pressure trends you can identify the behavior of each well and determine some issues, such as: choke erosion due to sand production, choke o tubing paraffin plugging or choke obstruction. In order to achieve an effective real-time monitoring of this type of wells, and in this way reduce the production losses, the challenge was to create online tools that could detect those mentioned issues.
The present work performs the analysis of wellhead pressure curves using data science, with the purpose of predicting real time anomalies that could occur for timely correction. The data correspond to 130 flowing wells from the Loma Campana Field. The study began with a filtering process of the pressure curve, with two specific objectives: first, eliminate atypical values from the time series, and second, smooth the curve in such a way that future predictions can be performed. Next, the Prophet methodology was applied with the purpose of predicting values of the curve. This is based on historicsl values of the time series to predict future values; the trend characteristic of the curve was used to apply this methodology. Then, to identify the anomaly a model was designed based on the declination of the curve. The pressure declination curve is a descending exponential function, so the first and second derivatives indicate the trend (ascending - descending) and curvature (concave or convex) of it. Once these values are available, they are classified according to the anomaly: paraffin, encrustation or obstruction. Finally, the model is being tested in the Loma Campana control room, delivering a probability of occurrence of any anomalies every hour.
Experimental Study of Gas-Liquid Diffusion in Porous Rocks and Bulk Fluids to Investigate the Effect of Rock Matrix Hindrance
Authors: Lou Xuanqing , Chakraborty Nirjhor , Karpyn Zuleima , Ayala Luis , Nagarajan Narayana , Wijaya Zein 
Published: September 2019

Abstract
The design of oil recovery processes by gas injection or vapor solvent relies on knowledge of diffusion coefficients to enable meaningful production predictions. However, lab measurements of diffusion coefficients are often performed on bulk fluids, without accountability for the hindrance caused by the pore network structure and tortuosity of porous media. As such, our ability to predict effective diffusion coefficients in porous rocks is inadequate and, additional laboratory work is needed to investigate the impact of the medium itself on transport by diffusion. In addition, experimental data on multi-phase diffusion coefficients are particularly scarce for tight rocks. This study therefore proposes an experimental methodology, based on a pressure-decay technique, to measure diffusion of injected gas in oil saturated porous rocks. A diffusion experiment of gas into bulk oil (without porous medium) provides an upper limit estimation of this gas-liquid diffusion coefficient. Diffusion experiments using limestone and Bakken shale provide insight into different degrees of restriction in high permeability versus low permeability media. Two analytical models and one numerical model were implemented and compared to determine the diffusion coefficients from the time-dependent experimental pressure-decay data. These diffusion coefficients were found in agreement with literature on corresponding data, demonstrating the validity of the modeling approaches used. Results indicate considerable hindrance to diffusion in porous media relative to bulk oil and relates to the tortuosity and constrictivity of the rock matrix. The diffusion coefficient of methane in bulk oil is 3.8 × 10−9 m2/s. In our limestone sample, this diffusion coefficient drops by one order of magnitude, ranging between 1.5 to 6.5 × 10−10 m2/s and, it drops by another order of magnitude in the Bakken shale sample to 2.0 × 10−11 m2/s.
Development of the Subsurface Process and Reinjection Compressor
Authors: Brady Jerry , Passmore Kevin , Paskvan Frank , Wilkes Jason , Allison Tim , Swanson Erik , Klein John 
Published: September 2019

Abstract
This paper discusses component developments, validation testing, and yard testing of the subsurface process and reinjection compressor (SPARC) prototype tool approaching downhole flowing conditions (≈1200 psig and &gt; 225°F). This is the first time a compressor and turbo expander have been built small enough to be run through tubing and operated autonomously from the surface.
A brief review of the overall system design and critical component design and testing are followed by a detailed review of the surface testing of the entire prototype machine at simulated downhole conditions. The SPARC concept uses the excess production pressure (energy) that is usually wasted across a choke or elsewhere in the production system to generate power through a downhole turbo-expander that runs a downhole gas compressor to reinject a portion of the gas stream. The system consists of a downhole separator, compressor, turbo-expander and other standard downhole equipment for the necessary plumbing.
The successful test results of the bearing and thrust disk component testing at up to 1,000 psig and &gt; 450°F are provided, followed by the successful yard test results of the entire SPARC prototype machine at downhole flowing conditions, including all the rotating equipment (turbo expander, compressor, and shaft), in situ process-lubrication system, and autonomous controls.
This equipment will allow for the reduction of costly surface facilities to process, compress, and reinject produced gas into North Slope fields and some oil and condensate fields elsewhere globally, which are limited in liquid hydrocarbon production because of surface gas processing facility limitations.
Another potential use of the SPARC technology is as an artificial lift mechanism for gas reservoirs. Using the SPARC as a gas well artificial lift system would require a redesign of the SPARC with an electric motor as its power source in place of the turbo-expander. However, no new technology breakthroughs are necessary because the technology has already been developed with the SPARC design.
To date, there have been no small gas compressors, turbo expanders, and other necessary equipment built and tested that can be run through 4 1/2-in. tubing/casing and operate autonomously at downhole conditions. This technology opens up the possibilities of additional relatively inexpensive gas recycling on the North Slope and other condensate fields worldwide.
Impact of Asymmetric Stimulated Rock Volume on Casing Deformation in Multi-Stage Fracturing; A Case Study
Authors: Yu Hao , Dahi Taleghani Arash , Lian Zhanghua , Lin Tiejun 
Published: September 2019

Abstract
Microseismic data and production logs in our study area have confirmed an asymmetric development of the stimulation rock volume, while severe casing deformation problems have been reported frequently in this area. In this paper, we investigate the possibility of casing failure due to strong shear stresses developed by asymmetric stimulated zones. Overlapping stimulation zones in adjacent stages may intensify asymmetry of the pore pressure distribution and resultant shear forces. Although induced shearing may have a positive impact on fracture permeability, but it may also cause operational problems by inducing severe casing deformations. While most of the casing deformation models only consider rock deformations very close to the wellbore, we developed a 3D coupled model for fracture network growth and stress re-distribution during hydraulic fracturing to achieve a more realistic model for casing deformation. This reservoir-scale model is tied to a more detailed near-wellbore model including the casing and cement sheath to simulate casing deformations. Case studies were conducted using data from a shale gas well that experienced severe casing deformation during hydraulic fracturing. Impact of stage spacing, and pumping rate are incorporated to investigate their potential impacts on casing and well integrity. Multi-stage hydraulic fracturing considering the development of complex fracture network is simulated at the reservoir scale based on the microseismic events. Continuous re-distribution and re-orientation of stress field near the borehole are tracked during the development of the fracture network which reveals some pocket of tensile stresses along the casing. Asymmetric fractures are observed to generate strong shear stress on the suspended casing. These shear forces result in deflection and S-shape deformations. Some regions receive repeating treatments, which leads to increase formation stress heterogeneity and worsen casing deformation severity. Our analysis has indicated that simply increasing the flexural strength by increasing thickness of casing cannot radically mitigate casing deformation problems. This paper provides a novel workflow for a coupled modelling of casing deformation during hydraulic fracturing operations, while current modelling efforts assume symmetric fracture geometries.