Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density.
The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively.
The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.
Zhang, Yingchun (CNOOC Research Institute Co., Ltd.) | Xu, Wei (CNOOC Research Institute Co., Ltd.) | Zou, Jingyun (CNOOC Research Institute Co., Ltd.) | Jing, Zhiyi (CNOOC Research Institute Co., Ltd.) | Fang, Lei (CNOOC Research Institute Co., Ltd.) | Liu, Jun (CNOOC International Limited)
In complex clastic reservoirs, deviation often exists in oil saturation derived from logging interpretation due to the borehole conditions and log quality. Especially in thin-sand reservoirs, oil saturation is generally lower than actual results because of boundary effect. An innovative approach of saturation height function coupled with rocktype is provided to improve the accuracy of saturation prediction in well logs and spatial distribution. The model results are compared with log derived results.
The new approach is based on the routine and special core analysis of over 100 core samples from the complex clastic reservoir in the north of Albert Basin in Uganda. Discrete rocktypes (DRT) are determined by flow zone index and pore throat radius which indicate the fluid flows. After converting the capillary pressure (Pc) data to reservoir conditions, Lambda curve fitting (Sw = A * PcB + C) is used to fit each capillary pressure curve. Then, a robust relationship between the fitting coefficients (A, B, C) and rock properties (i.e. porosity and permeability) is expressed as a nonlinear function for each DRT. Combined with the height above free water level, a water saturation (Sw) model is constructed by SHF within DRT model.
Using the porosity and permeability obtainedfrom routine core analysis, FZI and pore throat radius are calculated (e.g., by Winland function). Five different rocktypes (DRT1-5) are defined in the delta sand reservoir in the north of Albert Basin with distinct pore textures. The distinguishment is in accordance with the shape of capillary pressure curve, that is, the flow capability increases from DRT1 to DRT5. A strong correlation between Pc and Sw processed by Lambda curve is acquired for each core sample. Meanwhile, 3 coefficients A, B and C can be obtained in Lambda formula. By nonlinear regression, coherent relation between each factor and reservoir properties (porosity and permeability) for each DRT are obtained. Height above the free water level is estimated by geometrical modeling on the oil water contact. The Sw model is constructed by the new SHF function coupled with DRT model. It showed that the water saturation derived from SHF is highly consistent with log derived results and NMR results. Moreover, it provides more precise results in thinner sands and in spatial distribution.
Based on the identified different rocktype, a new SHF derived from capillary pressure data is utilized to establish the relationship between saturation, the height above the free water level and rock properties. The approach can significantly improve the accuracy of saturation prediction of thin reservoir and reasonably depict the spatial distribution characteristics of saturation. Furthermore, the approach will provide a more precise result in hydrocarbon volume calculation and numerical simulation.
Murdoch, Euan (Weatherford Completion Systems) | Walduck, Steve (Weatherford UK Ltd) | Munro, Chris (Weatherford UK Ltd) | Edwards, Andrew (Weatherford) | Choquet, Caroline (Weatherford Energy Services)
Successfully deploying a single trip completion system in a deep-water environment requires an innovative technical solution to address the risks that come with this environment. Following a request from the operator for a deep-water single trip solution, a number of different system options were proposed. Each system was evaluated against the operator’s requirements, and a Radio Frequency Identification (RFID) technology-based system was selected as it offered the greatest flexibility in both activation and contingency methods to meet the demands of the project.
It was proposed to hold a 2 stage System Integration Test (SIT) at a test rig in Aberdeen. The first SIT was performed with a small number of tools that could be setup in different modes to prove the system’s logic against the operator’s expectations. Whilst this was conducted successfully a number of learnings and operational optimisations were captured. These were fed into a full-scale SIT which was deployed at the same test rig. This second SIT involved a complete representation of the single trip system and was designed to test the final system logic prior to deployment into an offshore environment.
The system was then installed successfully in November 2018, on a subsea well, offshore Nigeria with no intervention. It resulted in an operational time saving of at least 60% over the previous best recorded time for a conventional two-trip completion from the same rig. This represented a step change in operational efficiency and will now be the operator’s base case completion methodology as they develop the field further.
This is the first time a single trip completion has been deployed in this fashion in a deep-water, offshore environment. The demonstrable step change in operational time and resultant project OPEX savings, proves that the use of RFID and remote actuated tools within completions offer excellent alternatives to traditional methods.
Reservoir fluid characterization is critical to understanding the nature and phase behavior of reservoir fluids. This process has typically been undertaken using laboratory analyses, a time-intensive and costly process which also provides compositional data. Over time, correlations have been developed to predict the PVT properties of crude oil based on parameters such as solution gas-oil ratio, saturation pressure, viscosity, and density. These correlations have had shortcomings such as utilizing a leave-one-out approach, or recently, focused on non-inferable methods such as Neural Networks. This work utilizes compositional data, hitherto neglected in PVT correlations, as input into an inferable machine learning algorithm which can be used to predict PVT properties of crude oil from the Niger Delta basin.
Data containing bubble point pressure, solution gas-oil ratio, and oil formation volume factor alongside composition were obtained and used to develop models. Machine learning model training techniques such as data preprocessing, transformation and hyper-parameter tuning were undertaken. The elastic net regression algorithm utilizing a cross-validation approach was used to develop the models. This ensured an adequate bias-variance tradeoff.
The resulting models were compared with established correlations such as Standing & Katz. Upon statistical analyses performed comparably. The bubble point pressure model, solution gas-oil ratio, oil formation volume factor achieved R-squared value of 0.87, 0.95 and 0.84 respectively on the validation dataset. The models are expressed in the form of equations which can be used in petroleum engineering calculations or implemented in reservoir simulation software. By implementing this approach, a framework for utilizing machine learning for Petroleum Engineering problems which produces inferable results is established. Given potential discoveries in the Niger Delta, upon obtaining compositional data, these set of equations can be used to predict the reservoir crude oil PVT properties, leading to savings in time, cost, and effort, while obtaining actionable and accurate results.
We develop a novel ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a machine-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of machine learning to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images.
We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.
Dong, Xuemei (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company) | Zhang, Ting (Surignan Operating Company, PetroChina Changqing Oilfield Company) | Yao, Weijiang (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company) | Hu, Tingting (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company) | Li, Jing (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company) | Jia, Chunming (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company) | Guan, Jian (Research Institute of Geophysical, Research Institute of Exploration and Development, PetroChina Xingjiang Oilfield Company)
Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum.
Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
It is often stated that necessity is the mother of invention. Never is this proverb more relevant than in the offshore oil and gas environment we currently operate in where real step changes leading to reduced capital and operational expenditure opportunities are sought and embraced by field operators. This paper discusses the pre-job planning, field execution and lessons learned from one such technology that challenged conventional thinking of sand faced completion, casedhole completion and well integrity to successfully deliver a single-trip, interventionless, sand control completion in deepwater Bonga Field, located on the continental slope of the Niger Delta.
Convention dictates that the vast majority of offshore completions be run in two and sometimes three trips which routinely takes in excess of eight to ten days to deploy. Given the day rate of high specification rigs capable of drilling in deep water environments, the ability to reduce this time was deemed paramount to the economics of the project. Utilizing a collaborative approach to initial concept design, risk assessment, extensive testing and contingency planning at component and system level, a single-trip, interventionless, sand control completion system was designed and successfully installed. This paper describes the completion architecture, operational sequence and challenges leading to the installation of an interventionless completion.
A clearly defined set of deliverables and design principles were drawn up to guide the direction of the project including: successfully deploying the upper and lower completion in one trip, and testing all barriers. Adopting a simple, low risk and high reward design, meeting clients well barrier requirements and utilizing proven cost-effective technology are examples of design principles used. The system was tested and evolved through a number of iterations in an onshore trial well environment on a number of occasions leading to the first successful deployment completed in the second half of 2018, resulting in an average completion installation time of 5 days, versus the average 10 days for deploying multi-trip completions. Details of the successful installations, lessons learned, along with planned future activity are outlined within the body of this paper. While several of the components incorporated in the single-trip system had been run previously in isolation, this paper also discusses the steps taken to facilitate the first full-system approach to the application of radio frequency identification (RFID) enabled tools in the first single-trip, interventionless sand control completion system. Several components within the completion have been equipped with this technology including a multi-cycle ball valve, wire wrapped screens fitted with inflow control device (ICD), remote operated sliding sleeve for annular fluid displacement.
Just as Nigeria gets to grips with militants who brought the nation’s oil industry to its knees a few years ago, another group of longstanding foes are slowly making a comeback: thieves. Recently, Nigeria's Department of Petroleum Resources issued guidelines in furtherance of the objectives of the Flare Gas (Prevention of Waste and Pollution) Regulations, 2018. Nigerian militants threatened on 17 January to attack offshore oil facilities within days, raising fears of a repeat of a 2016 wave of violence that helped push Africa’s biggest economy into recession. Is Crude Oil Killing Children in Nigeria? A recent report by a group of scientists at the University of St. Gallen in Switzerland found that children born within 10 km of an oil spill were twice as likely to die in their first month.
The well will immediately be brought on production and is expected to flow at more than 100 MMscf/D of gas and 3,000 B/D of associated condensate, the company said. The French major is racking up barrels of deepwater production as part of its large-scale West African push. Since 2007, an operator in Nigeria has registered a significant increase of oil-spill events caused by sabotage and oil-theft activities. The technology presented here allows detecting and locating leaks taking place at a distance from the sensor of up to 35 km.