The West Delta Deep Marine concession (WDDM) lies offshore in the Deep water of the present day Nile delta. WDDM consists of many Pliocene submarine channel complexes. The Serpent field is one of those slope marine channels and consists of two separate channels namely channel 12 and channel 13. Channel 12 is divided into three compartments by gravitational faults and channel 13 is composed of two compartments separated by stratigraphic barrier. Gas water contact (GWC) in channelized turbidities reservoir might create an intricate reservoir relationship. Gas water contact becomes complicated when the faults and the facies lateral change provide seals. Those hydrocarbon contacts depths become unpredictable without a distinct system to understand the cause of those variable contacts. Water break-through occurred earlier than expected in Serpent production wells as there was no proper modeling for reservoir facies heterogeneity and facies associated petrophysical parameters. A further compartmentalization of channel 12 arose as the sealing capacity of the gravitational faults cast a doubt over channel-12 compartmentalization and the connected gas initial in place (GIIP). The geological foreknowledge of Serpent field, the production issues and the dire need for further development plans in Serpent field were the motives to initiate this study. Integrated study was designed to answer the unsolved challenges of characterizing the reservoir heterogeneity and faults' sealing capacity. 3-D (three dimensional) high quality seismic data and different seismic attributes were integrated with different well data to build a robust 3-D static model. Static model was the way to elaborate the facies accurate distribution and the different petrophysical parameters in Serpent reservoir. In addition, the 3-D static model was used in the prediction of the faults' sealing capacity through the fault rock facies, fault rock petrophysical properties and transmissibility. In a nutshell, the resultant static model answered the field's issues regarding the early water production, facies heterogeneity and Successfully isolate the different reservoir compartments then run into prediction to assess the potential of the existing well-stock and any future development plans in Serpent field.
Stuck pipe has been recognized as one of the serious problems in drilling operations that has a significant impact on drilling efficiency and well costs. The events related to the stuck pipe can be responsible for losses of hundreds of millions of dollars each year in the drilling industry. This paper presents a study on the application of machine learning methodologies to predict the stuck pipe occurrence which can be utilized to modify drilling variables to minimize the likelihood of sticking. The new models were developed to predict the stuck pipe incidence for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The proposed models were examined using a few examples of real stick pipe cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. The present analysis supplies knowledge that can be used during well pre-planning and developmental phases to make informed decisions that will avoid pipe sticking problems and essentially optimize drilling performance. The risk of pipe sticking can then be minimized and the costs associated with its occurrence will be reduced.
Increased production alongside low costs has always been a long-standing goal for operators in a time where the industry is thriving to increase their operational and economical efficiencies. For maturing fields, operators are constantly seeking innovative techniques to access large quantities of reserves found in low permeability formations.
The nature of the reservoir has resulted in an economical need to implement a hydraulic fracturing campaign. This would assist with increased production through wider contact area of the producing interval. After a campaign resulting in favorable results through hydraulic fracturing, additional efficiencies addressing the operational and cost concerns of cemented plug & perf were required. In order to maintain production increases while maintaining operational efficiency, the need for multi-stage hydraulic fracturing became evident.
Six candidate wells were selected for proppant fracturing utilizing a multi-stage completion technique that has been widely adopted in North America for the past 17 years and internationally for over a decade. The technique utilizes a series of hydraulic mechanical packers and fracturing ports that are shifted open sequentially across the applicable zones targeted for treatment. The ports have seats that are sized to respective balls, which are used to isolate the stimulated intervals. The system allows for increased efficiency through eliminating the risks associated with wireline operations for perforating, or coiled tubing operations to mill out bridge plugs. It also allows for immediate and simultaneous production from all stages as well as contingencies for shutting off water zones. From June 2016 to March 2017, six systems were installed consisting of 6 to 8 hydraulic fracturing stages, which were placed in cased-hole pre-perforated wells.
This paper investigates reservoir quality assessment as well as the stimulation efficiency and productivity enhancement of the Sidri Field. It investigates two different completion methodologies to investigate operational efficiency and productivity enhancement of the Sidri Field in the Sinai Peninsula. The comprehensive evaluation will involve discussions of candidate selection, pre-job design alongside comparisons of timeline, cost, zonal isolation, and productivity to help serve as a guideline for operators in the region looking to enhance their completion approach.
Eldabbour, Mohamed (Abu Qir Petroleum) | Fadel, Ayman (Abu Qir Petroleum) | Soliman, Ali (Abu Qir Petroleum) | Safwat, Hatem (Abu Qir Petroleum) | Labib, Amr (Abu Qir Petroleum) | Belli, Andrea (Abu Qir Petroleum) | McLaughlin, Ryan (Corex U.K. Ltd) | Patey, Ian T.M. (Corex U.K. Ltd) | Munro, Murdo S. (Corex U.K. Ltd) | Jones, David (Corex U.K. Ltd)
Gravel pack completion operations are a sand production management technique that is considered successful if the well produces no sand and has minimal impact upon the potential productivity and hydrocarbon recovery. However, statistics show that many gravel packed wells suffer reduced productivity as a result of damaging mechanisms induced by gravel pack operations and completion fluids. This provides an opportunity for improved hydrocarbon recovery if the mechanisms are understood.
A study was conducted to simulate the alterations caused by the gravel pack operations including gravel carrier fluid, completion fluid and lost circulation material. Simulations using reservoir core samples were carried out at near-wellbore conditions, in order to examine operational fluid interactions with the reservoir and assess the impact of a stimulation fluid. Cores from a range of rock types were selected, and prepared to initial gas-leg saturation. An operational sequence consisting of completion fluid, gas production, stimulation fluid, completion fluid, and production of gas was carried out, with permeability measurements before and after the sequence.
In all core samples, the introduction of the completion fluid during gravel pack installation resulted in alterations of 30-60% reduction in core permeability. Geological interpretative analysis showed damage mechanisms including clay fines movement and pore blockage, dissolution of native cement, and retention of operational fluid in the pores. It was believed that retention of fluids was having the most significant impact upon permeability. Stimulations were carried out for all samples to quantify the effect of acid on removing the formation damage resulting from the gravel pack operations. The experiments showed 5-10% improvement on average except for one core sample, which showed 40% improvement.
Based upon the previous results, a modified sequence was examined, utilizing an alternative stimulation fluid/acid sequence and adding an extra operational stage. The experiments showed that after treatment an improvement of around 10% was noted, and after an additional stage, a further 8% improvement was seen. The final permeability was over 80-90% of the initial permeability, indicating that there was the potential for good productivity and recovery of hydrocarbons.
The results of the study were applied to seven gravel pack jobs in three wells and the field results showed the reduction in productivity after gravel pack installation was around only 10%, compared to previous wells which showed more than 50% reduction in productivity.
Abbas, Ahmed K. (Iraqi Drilling Company) | Flori, Ralph (Missouri University of Science and Technology) | Almubarak, Haidar (Missouri University of Science and Technology) | Dawood, Jawad (Basra Oil Company) | Abbas, Hayder (Missan Oil Company) | Alsaedi, Ahmed (Missouri University of Science and Technology)
Stuck pipe is still a major operational challenge that imposes a significant amount of downtime and associated costs to petroleum and gas exploration operations. The possibility of freeing stuck pipe depends on response time and subsequent surface action taken by the driller during and after the sticking is experienced. A late and improper reaction not only causes a loss of time in trying to release stuck pipe but also results in the loss of an important portion of expensive tubular, downhole equipment and tools. Therefore, a fast and effective response should be made to release the stuck pipe. Investigating previous successful responses that have solved stuck pipe issues makes it possible to predict and adopt the proper treatments. This paper presents a study on the application of machine learning methodologies to develop an expert system that can be used as a reference guide for the drilling engineer to make intelligent decisions and reduce the lost time for each stuck pipe event.
Field datasets, including the drilling operation parameters, formation type, and fluid mud characteristics, were collected from 385 wells drilled in Southern Iraq from different fields. The new models were developed to predict the stuck pipe solution for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. These machine learning methods offer insights that could improve response time and strategies for treating stuck pipe.
Africa (Sub-Sahara) Aminex Petroleum Egypt (APE), a subsidiary of UK-based Aminex, discovered oil at its South Malak-2 (SM2) well on the West Esh el Mellaha-2 concession in Egypt. Tests showed flow rates of approximately 430 B/D of 40 API gravity crude oil. Based on the findings at SM2, a full field development program will be presented to the Egyptian authorities and the joint venture partners before commercial development. APE is the operator of the license with partner Groundstar Resources. Foxtrot International discovered oil and gas at its Marlin North-1 well in Block CI-27, offshore Cote d'Ivoire. A 22-m perforated section of a gas-bearing column in a Turonian interval flowed at a stabilized rate of 25 MMcf/D of gas and 150 B/D of condensate through a 46/64-in.
Africa (Sub-Sahara) ExxonMobil subsidiary Esso Exploration Angola has started oil production at the Kizomba Satellites Phase 2 project offshore Angola. The project involves the development of subsea infrastructure for the Kakocha, Bavuca, and Mondo South fields. Mondo South is the first field to begin production, and the other two satellite fields will follow later this year. The goal is to increase Block 15's production to 350,000 BOPD. Esso (40%) is the operator with BP Exploration Angola (26.67%), Kosmos Energy discovered gas at the Tortue West prospect in Block C-8 offshore Mauritania.
Africa (Sub-Sahara) Eni discovered gas and condensate in the Nkala Marine prospect offshore Congo. The discovery could hold from 250 MMBOE to 350 million MMBOE in place, the company said. In a production test, the Nkala Marine 1 discovery well in the Marine XII block yielded more than 10 MMcf/D of gas and condensate. Eni is the operator with a 65% interest in the block. The remaining shares are held by New Age (25%) and Societé Nationale des Pétroles du Congo (SNPC) (10%). Sonangol and Total will break ground on a deepwater oil pumping project that will increase Angola's production by more than 30,000 B/D.
Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation pore pressure, and in-situ stresses of the studied area were included as inputs. The second step was by optimizing the process using a genetic algorithm (GA), as a class of optimizing methods for complex functions, to obtain the maximum ROP along with the related wellbore trajectory (AZI and INC). Finally, the suggested azimuth (AZI) and inclination (INC) are premeditated by considering the results of wellbore stability analysis using wireline logging measurements, core and drilling data from the offset wells.
The results showed that the optimized wellbore trajectory based on wellbore stability analysis was compatible with the results of the genetic algorithm (GA) that used to reach higher ROP. The recommended orientation that leads to maximum ROP and maintains the stability of drilling deviated wells (i.e., inclination ranged between 40°—50°) is parallel to (140°—150°) direction. The present study emphasizes that the proposed methodology can be applied as a cost-effective tool to optimize the wellbore trajectory and to calculate approximately the drilling time for future highly deviated wells.
The advanced technology has made directional drilling widely used to enhance the production of mature fields. The rate of penetration (ROP) contributes strongly towards the cost of drilling operations, where achieving higher ROP leads to substantial cost saving. The main objective of this study is to develop a model that predicts the ROP for deviated wells using artificial neural networks (ANNs).
The model was developed based on the most critical variables affecting ROP using ANNs. In addition to the azimuth and inclination of the well trajectory, the controllable drilling parameters, unconfined compressive strength (UCS), pore pressure, and in-situ stresses of the studied area were included as inputs. 1D Mechanical earth modeling (1D-MEM) data, geophysical logs, daily drilling reports, and mud logs (master logs) of deviated wells drilled in Zubair field located in Southern Iraq were used to develop the ANN model.
The results displayed that the ANN’s outputs are close to the measured field data. The correlation coefficient (R) and average absolute percentage error (AAPE) were over 0.91 and 8.3%, respectively, for the training dataset. For testing data, the developed model achieved a reasonable correlation coefficient (R) of 0.89 and average absolute percentage error (AAPE) of 9.6%. Unlike previous studies, this paper investigates the effect of well trajectory’s (azimuth and inclination) and their influence on the ROP for deviated wells. The major advantage of the present study is calculating approximately the drilling time of the deviated well and eventually reducing the drilling cost for future neighboring wells.