Bringing the Best of Sampling While Drilling in Highly Deviated S-Profile Wells: Case Studies from a Brown Field, Sabah, Offshore Malaysia

2021 ◽  
Author(s):  
Adif Azral Azmi ◽  
Nur Ermayani Abu Zar ◽  
Raja Azlan Raja Ismail ◽  
Nadia Zulkifli ◽  
Nikhil Prakash Hardikar ◽  
...  
Keyword(s):  
Resource Assessment ◽  
Empirical Modeling ◽  
Fluid Identification ◽  
Well Completion ◽  
Reservoir Fluid ◽  
Unconsolidated Sand ◽  
Pump Rate ◽  
Production History ◽  
Drilling Operations ◽  
Pressure Regime

Abstract Sampling While Drilling has undergone significant changes since its advent early this decade. The continuum of applications has primarily been due to the ability to access highly deviated wellbores, to collect PVT quality and volume of formation fluids. The increased confidence is also a result of numerous applications with varied time-on-wall without ever being stuck. This paper demonstrates the contribution of this technology for reservoir fluid mapping that proved critical to update the resource assessment in a brown field through three infill wells that were a step-out to drill unpenetrated blocks and confirm their isolation from the main block of the field. As a part of the delineation plan, the objective was to confirm the current pressure regime and reservoir fluid type when drilling the S-profile appraisal wells with 75 degrees inclination. Certain sand layers were prone to sanding as evidenced from the field's long production history. Due to the proven record of this technology in such challenges, locally and globally, pipe-conveyed wireline was ruled out. During pre-job planning, there were concerns about sanding, plugging and time-on-wall and stuck tools. Empirical modeling was performed to provide realistic estimates to secure representative fluid samples. The large surface area pad was selected, due to its suitability in highly permeable yet unconsolidated formations. For the first well operation, the cleanup for confirming formation oil began with a cautious approach considering possible sanding. An insurance sample was collected after three hours. For the next target, drawing on the results of the first sampling, the pump rate was increased early in time, and a sample was collected in half the time. Similar steps were followed for the remaining two wells, where water samples were collected. Oil, water, and gas gradients were calculated. Lessons learnt and inputs from Geomechanics were used in aligning the probe face and reference to the critical drawdown pressure (CDP). A total of 4,821 feet (1,469 meters) was drilled. 58 pressures were acquired, with six formation fluid samples and five cleanup cycles for fluid identification purpose. The pad seal efficiency was 95%. The data provided useful insights into the current pressure regime and fault connectivity, enabling timely decisions for well completion. The sampling while drilling deployment was successful in the highly deviated S-profile wells and unconsolidated sand, with no nonproductive time. Because of the continuous circulation, no event of pipe sticking occurred, thereby increasing the confidence, especially in the drilling teams. The sampling while drilling operations were subsequent, due to batch drilling, with minimal time in between the jobs for turning the tools around. The technology used the latest generation sensors, algorithms, computations and was a first in Malaysia. The campaign re-instituted the clear value of information in the given environment and saving cost.

A New Approach To Estimating Ultimate Recovery for Multistage Hydraulically Fractured Horizontal Wells by Utilizing Completion Parameters Using Machine Learning

2021 ◽  
pp. 1-16
Author(s):  
Sulaiman A. Alarifi ◽  
Jennifer Miskimins
Keyword(s):  
Oil And Gas ◽  
Horizontal Wells ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
New Approach ◽  
History Data ◽  
Well Completion ◽  
Production History ◽  
Early Production ◽  
Fractured Horizontal Wells

Summary Reserves estimation is an essential part of developing any reservoir. Predicting the long-term production performance and estimated ultimate recovery (EUR) in unconventional wells has always been a challenge. Developing a reliable and accurate production forecast in the oil and gas industry is mandatory because it plays a crucial part in decision-making. Several methods are used to estimate EUR in the oil and gas industry, and each has its advantages and limitations. Decline curve analysis (DCA) is a traditional reserves estimation technique that is widely used to estimate EUR in conventional reservoirs. However, when it comes to unconventional reservoirs, traditional methods are frequently unreliable for predicting production trends for low-permeability plays. In recent years, many approaches have been developed to accommodate the high complexity of unconventional plays and establish reliable estimates of reserves. This paper provides a methodology to predict EUR for multistage hydraulically fractured horizontal wells that outperforms many current methods, incorporates completion data, and overcomes some of the limitations of using DCA or other traditional methods to forecast production. This new approach is introduced to predict EUR for multistage hydraulically fractured horizontal wells and is presented as a workflow consisting of production history matching and forecasting using DCA combined with artificial neural network (ANN) predictive models. The developed workflow combines production history data, forecasting using DCA models and completion data to enhance EUR predictions. The predictive models use ANN techniques to predict EUR given short early production history data (3 months to 2 years). The new approach was developed and tested using actual production and completion data from 989 multistage hydraulically fractured horizontal wells from four different formations. Sixteen models were developed (four models for each formation) varying in terms of input parameters, structure, and the production history data period it requires. The developed models showed high accuracy (correlation coefficients of 0.85 to 0.99) in predicting EUR given only 3 months to 2 years of production data. The developed models use production forecasts from different DCA models along with well completion data to improve EUR predictions. Using completion parameters in predicting EUR along with the typical DCA is a major addition provided by this study. The end product of this work is a comprehensive workflow to predict EUR that can be implemented in different formations by using well completion data along with early production history data.

A new method for reservoir fluid identification

2006 ◽  
Vol 3 (2) ◽  
pp. 124-129 ◽  
Author(s):  
Wenzheng Yue ◽  
Guo Tao
Keyword(s):  
New Method ◽  
Fluid Identification ◽  
Reservoir Fluid

Reduction of Wellbore Effects on Gas Inflow Evaluation Under Underbalanced Conditions

SPE Journal ◽  
2008 ◽  
Vol 13 (02) ◽  
pp. 216-225 ◽  
Author(s):  
Olukayode J. Aremu ◽  
Samuel O. Osisanya
Keyword(s):  
Drilling Fluid ◽  
Inflow Rate ◽  
Gas Reservoir ◽  
Flow Rates ◽  
Underbalanced Drilling ◽  
Reservoir Fluid ◽  
Wellbore Storage ◽  
Outflow Rate ◽  
Drilling Operations ◽  
Fluid Inflow

Summary Wellbore storage effects have been identified to significantly smear the accuracy of evaluating reservoir productivity through the fluid outflow rate from the annulus during underbalanced drilling. Such effects have continuously introduced considerable errors in characterizing the reservoir during underbalanced drilling. Conceptually, because of the ready volume-changing ability of the gas, wellbore storage becomes a determining factor during underbalanced drilling of a gas reservoir. Wellbore storage could either cause decrease (unloading effects) or increase (loading effects) in the annular gas density, depending on the choke opening procedures. Correspondingly, annular fluid outflow rate is considerably affected. Because it is practically difficult to deduct the fluid-flow rate attributable to the wellbore storage from the total fluid outflow rate, reducing the influence of wellbore effects on the evaluation of gas-reservoir productivity is presented in this study. Volumetric production analysis at the wellbore-sand face is introduced through a mathematical modeling of inflow of gas bubbles into the wellbore. This mathematical modeling utilizes forces such as the viscous force, drilling fluid ejecting forces from the bit nozzles, buoyancy, interfacial tension, and gas-reservoir forces for its analyses. Some analytical results that are overshadowed by wellbore storage are presented and supported by extensive experimental studies. Introduction One of the derivable benefits from underbalanced drilling is the ability to evaluate the productivity of a reservoir during drilling operations (Beiseman amd Emeh 2002). Other benefits include little to no invasive formation damage; higher penetration rate, especially in hard rocks; and lower cost of drilling operations if underbalanced drilling could consistently be maintained (Bennion et al. 2002). However, from the real-time bottomhole pressure measurements taken while drilling, it is obvious that continuous maintenance of underbalanced conditions at the bottomhole is difficult. Pressure surges that occur during some subsidiary operations such as pipe connections and surveys tend to jeopardize the avoidance of invasive formation damage (Yurkiw et al. 2002). From the recent literature, reservoir evaluation has been approached through the estimation of the reservoir fluids flow rates into the wellbore. Assumption of the reservoir fluid inflow rate being the difference in the drilling fluid surface injection rate and the fluid outflow rate from the annulus has consistently been used (Kardolus and van Kruijsdijk 1997; Larsen and Nilsen 1999; Hunt and Rester 2000; Kneissl 200l; Lorentzen et al. 2001; Vefring et al. 2002; Biswas et al. 2003). So far, efforts in modeling reservoir fluid inflow have been concentrated on the oil inflow (Kardolus and van Kruijsdijk 1997; Larson and Nilsen 1999; Hunt and Rester 2000; Kneissl 200l; Lorentzen et al. 2001; Vefring et al. 2002; Biswas et al. 2003). These present approaches to production evaluation and characterization of gas formation recognize the important effects of wellbore phenomena, but have not been able to provide adequate means of reducing the influences. These wellbore phenomena include the gas-bubble coalescence and breakage, and bubble expansion and compression that are not possible to practically quantify during bubble annular upward flow. Because the present approaches involve the comparison of the surface fluid injection rate with the annular outflow rate, the influence of these phenomena on the gas formation evaluation is inevitable. Unfortunately, all of these wellbore phenomena cause additional annular flow rates that cannot be individually and practically measured, and thus the reservoir fluid inflow rate at the bottomhole cannot be practically modified for their influences. Not recognizing the impact of such additional annular flow rates could cause misjudgment of the inflow capabilities of the gas reservoir. In order to properly alleviate these effects on gas-inflow analyses, a volumetric production analysis at the wellbore-sand face contact is presented in this study. The conduction of gas-inflow analyses have been similarly performed as the liquid inflow in the petroleum engineering sectors. Practically speaking, gas inflow into a denser fluid system is bubbly in character, while liquid inflow is streaky. It is, therefore, proper to mathematically couple the forces of the viscosity, surface tension, inertia, and buoyancy that are responsible for gas-bubble formation or development to the drilling-fluid-ejecting forces from the bit nozzles and the reservoir forces in modeling gas-inflow scenarios. Therefore, with the existence of underbalanced pressure conditions at the bottomhole, the modeling procedures presented in this study could be used for predicting the total volume of gas inflow with significantly reduced wellbore effects while drilling. This is possible as long as an underbalanced condition is maintained at the bottomhole. This is a computer-simulation approach that utilizes real-time surface measurable underbalanced drilling data to predict quantitative gas volumes at the wellbore-sand face during drilling. As an additional advantage, the analyses do not involve knowing the gas inflow rate at the sand face, which could be difficult to accurately measure during underbalanced drilling operations. Standard engineering concepts are used to estimate downhole conditions for the analyses. Among the benefits from this study are reduced influences of the wellbore effects on the evaluation of gas-reservoir volumetric productivity during underbalanced drilling, the revealing of possible greater near-wellbore damage in some gas reservoirs, and possible in-situ permeability impairment through pore space compression.

Mud-Gas Breakthrough Equinor Develops Real-Time Reservoir-Fluid Identification

2021 ◽  
Vol 73 (02) ◽  
pp. 37-39
Author(s):  
Trent Jacobs
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Oil And Gas ◽  
Partial Information ◽  
Learning Model ◽  
Identification System ◽  
Fluid Identification ◽  
Reservoir Fluid ◽  
Machine Learning Model ◽  
The Difference

For all that logging-while-drilling has provided since its wide-spread adoption in the 1980s, there is one thing on the industry’s wish list that it could never offer: an accurate way to tell the difference between oil and gas. A new technology created by petrotechnicals at Equinor, however, has made this possible. The innovation could be thought of as a pseudo-log, but Equinor is describing it as a reservoir-fluid-identification system. Using an internally developed machine-learning model, it compares a database of more than 4,000 reservoir samples against the real-time analysis of the mud gas that flows up a well as it is drilled. Crunched out of the technology’s various hardware and software components is a prediction on the gas/oil ratio (GOR) that the rock being drilled through will have once it is producing. Since this happens in real time, it boils down to an alert system for when drillers are tapping into uneconomic pay zones. “This is something people have tried to do for 30 years - using partial information to predict entire oil and gas properties,” said Tao Yang. He added that “the data acquisition is rather cheap compared with all the downhole tools, and it doesn’t cost you rig time,” highlighting that the mud-gas analyzer critical to the process sits on a rig or platform without interfering with drilling operations. Yang is a reservoir technology specialist at Equinor and one of the authors of a technical paper (SPE 201323) about the new digital technology that was presented at the SPE Annual Technical Conference and Exhibition in October. He and his colleagues spent more than 3 years building the system which began in the Norwegian oil company’s Houston office as a project to improve pressure/volume/temperature (PVT) analysis in tight-oil wells in North America. It has since found a home in the company’s much larger offshore business unit in Stavanger. Offshore projects designed around certain oil-production targets can face harsh realities when they end up producing more associated gas than expected. It is the difference between drilling an underperforming well full of headaches and one that will pay out hundreds of millions of dollars over its lifetime. By introducing real-time fluid identification, Equinor is trying to enforce a new control on that risk by giving drillers the information they need to pull the bit back and start drilling a side-track deeper into the formation where the odds are better of finding higher proportions of oil or condensates. At the conference, Yang shared details about some of the first field implementations, saying that in most cases the GOR predictions made by the fluid-identification system were confirmed by traditional PVT analysis from the trial wells. Unlike other advancements made on this front, he also said the new approach is the first of its kind to combine such a large database of PVT data with a machine-learning model “that is common to any well.” That means “we do not need to know where this well is located” to make a GOR prediction, said Yang.

Reservoir Fluid Identification by the Wavelet Transform

2003 ◽  
Vol 46 (6) ◽  
pp. 1241-1250 ◽  
Author(s):  
Wenzheng YUE ◽  
Guo TAO
Keyword(s):  
Wavelet Transform ◽  
Fluid Identification ◽  
Reservoir Fluid

Reservoir fluid identification and elastic modulus determination using AVO

1996 ◽  
Vol 35 (2-3) ◽  
pp. 159-165 ◽  
Author(s):  
D. Santoso ◽  
S. Alam ◽  
L. Hendrajaya ◽  
Alfian ◽  
S. Munadi ◽  
...  
Keyword(s):  
Elastic Modulus ◽  
Fluid Identification ◽  
Reservoir Fluid
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