This course provides a fundamental understanding of process safety techniques and how applying these techniques can improve safety, equipment reliability, environmental performance and reduce overall costs. It presents an overview of the elements comprising process safety, practical examples and how process safety can be integrated into day-to-day operations. Working and studying abroad is a huge part of the oil and gas industry and despite the impact on a professional’s career and personal life, little guidance is available for those considering the big move. At this event, we will be sharing stories from those who have gone through the same process and explore some of the benefits and difficulties of diverse working environments. Sustainability means many different things to different people. For governments, it means ensuring development that meets the needs and aspirations of the present without compromising the ability of future generations to meet their own needs.
Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
Plunger lifted, and free-flowing gas wells experience a wide range of issues and operational inefficiencies such as liquid-loading, downhole and surface restrictions, stuck or leaking motor control valves, and metering issues. These issues can lead to extended downtime, equipment failures, and other production inefficiencies. Using data science and machine-learning algorithms, a self-adjusting anomaly detection model considers all sensor data, including the associated statistical behavior and correlations, to parse any underlying issues and anomalies and classifies the potential cause(s). This paper presents the result of a Proof of Concept (PoC) study conducted for a South Texas operator encompassing 50 wells over a three-month period. The results indicate an improvement compared to the operators' visual inspection and surveillance anomaly detection system. The model allows operators to focus their time on solving problems instead of discovering them. This novel approach to anomaly detection improves workflow efficiencies, decreases lease operating expenses (LOE), and increases production by reducing downtime.
Production from highly paraffinic crude oil wells poses unique technical challenges such as poor flowability and paraffin deposition on the production tubing. Paraffin deposition increases the lift load on the pump, reduces pump efficiency, and eventually plugs the pump. To restore the productivity of these wells a common solution is to inject hot oil or hot water at 160°F–200°F to clean the deposits. This process imposes higher operating cost and lost production due to well downtime.
Paraffin inhibitor (PI) and pour point depressant (PPD) have been used to treat paraffinic fluids but are not effective for wells with high water cuts. These wells when treated with PI/PPD still require high cost maintenance such as the hot oil/water jobs and/or well workover. This paper presents a more effective treatment using tailored chemical mixtures to form a water dispersion with the paraffinic oil, thus to increase oil flowability and reduce deposition. A novel test method has been developed to evaluate effectiveness of treatment chemicals on various paraffinic oils based on flowability and cleanliness. The test method has been validated with field trial data from three different wells in the Uinta Basin, Utah and Julesburg Basin, Colorado.
The results of the field trials showed a significant increase in pumping efficiency and crude oil production. Need for hot water application was also reduced or eliminated for the treated wells. Improved oil and produced water quality were also observed. These results demonstrated that the water dispersion-based treatment is a more effective treatment for high paraffin wells with high water cuts.
A major shale producer in North America with established oil and gas production was facing challenges with severe paraffin deposition in downhole tubing and flowlines. Since chemical recommendations based on traditional screenings failed to deliver adequate inhibition, the operator turned to a costly remediation program to maintain production. We aimed to revisit the case, do a root cause analysis, and look for a potential chemical solution for cost savings. The field deposit obtained from the producer proved to be quite complex and introduced limitations with our current internal HTGC method for carbon chain analysis. Upon analysis, components present in the sample were found to exceed the solidity limits of the carrier system, carbon disulfide (CS2) and would precipitate out of the solution and form a two-phased system. These components were believed to be higher molecular weight carbon chains (HMWC) above C70+ at a high enough concentration to exceed the solvents solubility limit. This was the first time encountering such a sample in our experience. A systematic approach was applied to isolate the insoluble HMWC and further outsourced analysis. A MALDI-TOF and High-Resolution Carbon-13 NMR was utilized to confirm the presence of C90+ chains within the deposit at a high enough concentration to have a trimodal paraffin distribution system. To our knowledge, this is the first time a trimodal system has been documented.
Reliable estimation of organic matter characteristics is essential in drilling decisions, source rock evaluation, and unconventional reservoir production. Their measurement is based on experiments after core sampling, which is time-consuming and economically challenging. In this study, we present a new approach to evaluate the characteristics of organic matter in source and reservoir rocks by in-situ electrical heating and temperature transient analysis under in-situ conditions.
The new approach is based on inverse modeling, which monitors in-situ heater temperature during electrical heating and machine learning technologies. Thermal method of electrical heating is applied for the in-situ pyrolysis, to figure out the characteristics of organic matter—kerogen volume fraction and activation energy of decomposition reaction. The heater temperature acts as an indicator of type and maturity of kerogen, since it is affected by the bulk thermal conductivity of formation, which is a function of dynamically changing rock-and-pore composition by kerogen decomposition. A full-physics simulation model of in-situ kerogen pyrolysis is used to generate output data of electrical heater temperature, which is the input data of learning-based models. Minimal simplification of physical and chemical phenomena in the full-physics simulation model, which describes the multicomponent-multiphase-nonisothermal systems involving kinetic reactions, gives the confidence of synthetic output data of heater temperature.
Full-physics simulation model computes system responses under unknown and uncertain input parameters, which determine the reactivity of kerogen pyrolysis. The full-physics simulation model generates the sets of heater temperature transient data while heating with constant heat flux, in the 300 different simulated source rocks containing Types 1, 2, and 3 kerogens with various organic matter content and activation energies. Based on the set of heater temperature transient data as input parameters, Artificial Neural Network (ANN) is employed to generate a black box model to estimate the unknown organic matter content and activation energy. Developed ANN data-driven model shows better performance in estimating unknown parameters, in Types 2 and 3 kerogens with wide ranges of activation energies than Type 1 kerogen with a narrow range of activation energy. Support Vector Machines (SVM) method, which categorizes data into multiple classes by using hyperplanes, is applied to classify the heater temperature transient data into different types of kerogens and shows good performance in classification.
The new characterization technology of in-situ organic matter in source rocks presented in this study provides reliable information of types and maturity of organic matter, without experiments after core sampling. It is expected to enable the realistic evaluation of source rocks under subsurface conditions, by resolving technical and economic challenges.
Study made from the results observed over a particular application objective with one of the recently developed proppant fracturing techniques known as Channel Fracturing. This technique was used in this application to place a proppant fracturing treatment in a tight gas reservoir which pushes the installed well completion to reach its mechanical limit capabilities. Channel (or pillar) fracturing was applied in multiple cases with the intention to constrain the pressure increase commonly observed during a fracture job execution.
The development of unconventional resources is capital intensive and challenging where operators spend a large amount of resources to maximize value. This is a direct result of completing thousands of wells with multistage fracturing. The optimization of well completion to enhance hydrocarbon recovery will help to reduce development costs and enhance project economics under the uncertainty parameters: geological, engineering, and economic.
The paper demonstrates a novel workflow as an effective way to optimize completion design by integrating advanced multi-stage fracture modeling with reservoir simulation in an unconventional resource play. This work shows an integrated workflow using a compositional dynamic simulation study for gas condensate well. The complexity of gas flow physics in both nano-darcy reservoir as well as hydraulically fractured Stimulated Rock Volume (SRV) are considered. The physics include gas desorption, pressure dependent permeability, non-Darcy flow and gas condensate fluid behavior.
The workflow includes QA/QC of the geologic model with a fine model resolution to map the hydraulic fractures. Long-term flow back data is used to calibrate the simulation model using history matching regions following the analytical trilinear model. After achieving a reasonable history matching, a detailed uncertainty assessment was performed to estimate P10, P50 and P90 of the well's EUR (Estimated Ultimate Recovery) using Proxy modeling workflow. Uncertainty parameters include hydraulic fracture half-length, SRV permeability, dew point pressure, under-saturated desorption pressure, rock compaction trend, etc.
Finally, what-if scenarios were performed to assess the impact of cluster spacing, fracture height, horizontal well length and minimum well head pressure (WHP) on the well's EUR.
The results of this work illustrates the workflow used to optimize well completion design including the number of stages along the lateral, length of the lateral, treatment sizes and how it impacts well performance as well to support management decision making.