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Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Africa (Sub-Sahara) Oil samples have been recovered in the FAN-1 exploration well, being drilled offshore Senegal. Elevated gas and fluorescence were encountered in a shallow secondary target, and the presence of oil was confirmed by an intermediate logging program. Oil samples from thin sand were collected by a wireline formation tester for further analysis. The well will be deepened to a planned total depth of approximately 5000 m. Cairn is the operator (40%), with partners ConocoPhillips (35%), FAR (15%), and Senegalese national oil company Petrosen (10%). A drillstem test of BG Group's Mzia-3 well--located in Block 1, offshore southern Tanzania, at a water depth of around 1800 m--reached a maximum sustained flow rate of 101 MMscf/D of natural gas. The Mzia prospect is a multilayered field of Upper Cretaceous age with a gross gas column estimated at more than 300 m.
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We investigate the effect of heterogeneous petrophysical properties on Low Salinity Water Flooding (LSWF). We considered reservoir scale models, where the geological properties were obtained from a giant Middle East carbonate reservoir. The results are compared against a typical sandstone model.
We simulated low salinity induced wettability changes in field scale models in which the petrophysical properties were randomly distributed with spatial correlation. We examined a wide range of geological realisations which mimic complex geological structures. Sandstone was simulated using a log-linear porosity-permeability relation with fairly good correlation. A carbonate reservoir from the Middle East was simulated where a much less correlated porosity permeability relationship was obtained. The salinity of formation water was set to typically observed values for the sandstone and carbonate cases. A number of simulations were then carried out to assess the flow behaviour.
We have found that the general trend of permeability-porosity correlation has a key role that could mitigate or aggravate the impact of spatial distributions of petrophysical properties. We considered models with a log-linear permeability-porosity correlation, as generally observed for sandstone reservoirs. These are likely to be directly affected by the spatial distribution more than models with a power permeability-porosity correlation, which is often reported for flow units of carbonate reservoirs. The scatter of data in the permeability-porosity correlations had a relatively small impact on the flow performance. On the other hand, the effect of heterogeneity decreases with the width of the effective salinity range. Thus, uncertainty in carbonate reservoirs arises due to the ambiguity of spatial distribution of permeability and porosity would be less affects the LSWF predictability than in sandstone case. Overall, the incremental oil recovery due to LSWF was higher in the carbonate models than the sandstone cases. We observe from uncertainty analysis that the formation waterfront was less fingered than the low salinity waterfront and the salinity concentration. The dispersivity of salinity front and the water cut can be estimated for models with various degrees of heterogeneity.
The outcome of the study is a better understanding of the implications of heterogeneity on LSWF. In some cases the behaviour can appear like a waterflood in very heterogeneous cases. It is important to assess the reservoir effectively to determine the best business decision.
Facies classification is significant for characterization and evaluation of a reservoir because the distribution of facies has an important impact on reservoir modelling which is important for decision making and maximizing return. Facies classification using data from sources such as wells and outcrop cannot capture all reservoir characterization in the inter-well region and therefore as an alternative approach, seismic facies classification schemes have to be applied to reduce the uncertainties in the reservoir model. In this research, a machine learning neural network was introduced to predict the lithology required for building a full field earth model for carbonate reservoirs in Sothern Iraq.
In the present research, multilayer feed forward network (MLFN) and probabilistic neural network (PNN) were undertaken to classify facies and its distribution. The well log that was used for litho-facies classification is based on a porosity log. The spatial distribution of litho-facies was validated carefully using core data. Once successfully trained, final results show that PNN technique classified the carbonate reservoir into four facies, while the MLFN presented two facies. The final results on a blind well, show that PNN technique has the best performance on facies classification. These observations implied this reservoir consists of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.
The work and the methodology provide a significant improvement of the facies classification and revealed the capability of probabilistic neural network technique when tested against the neural network. Therefore, it proved to be very successful as developed for facies classification in carbonate rock types in the Middle East and similar heterogeneous carbonate reservoirs.
Integrating discrete facies classification into the estimation of formation permeability is a crucial step to improve reservoir characterization and to preserve heterogeneity quantification. Therefore, it is essential to obtain the most accurate estimation of permeability in non-cored intervals in order to attain realistic reservoir characterization and modeling. In our most recent paper [OTC-30906-MS], the electrofacies classifications have been conduced for a well from a carbonate reservoir in a Giant Southern Iraqi oil field. The same predicted discrete electrofacies distribution was included in this paper along with well logging interpretations to model and predict the reservoir core permeability for all wells. The well logging interpretations that were included in permeability modelling are neutron porosity, shale volume, and water saturation as a function of depth. The regression and machine learning approaches adopted for permeability modelling are multiple linear regression (MLR), smooth generalized additive Modeling (SGAM) and Random Forest (RF) Algorithm. The classified electrofacies were considered as a discrete independent variable in the core permeability modelling to provide different model fits given each electrofacies type in order to capture the different permeability variances.
The matching visualization between the observed and predicted core permeability, the computed root mean square prediction error and adjusted squared R were considered as validation and accuracy tools to compare between the three modelling approaches. Since there are too many intervals with missing core permeability measurements, the modelling was first adopted on the intervals that have permeability readings (known subset). The prediction was then conducted given the same known permeability intervals in addition to the entire dataset (full dataset). The root mean square prediction error and adjusted squared R for the Random Forest were significantly better than in both MLR and SGAM for the known subset and full dataset. It can be concluded that combining the electrofacies in one permeability model has accurate, fast and an automation procedure of prediction for other wells. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
Lai, Jie (Southwest Petroleum University) | Wang, Kun (Southwest Petroleum University) | Zhou, Hangyu (Southwest Petroleum University) | Zhao, Junsheng (Middle Sichuan Gas Field of Southwest Oil & Gasfield Company, PetroChina) | Wu, Lin (Middle Sichuan Gas Field of Southwest Oil & Gasfield Company, PetroChina)
Acidizing is a popular stimulation technique for carbonate reservoirs. Due to strong heterogeneity of the pore distribution, it is not easy for us to know where the acid would go through once it was injected into the rock sample. With the help of low-field Nuclear Magnetic Resonance (NMR) technique, we can monitor the pore structure variation during acid coreflooding, find out which kind of pores the acid prefers to enter and the mechanism of acidizing, and finally, provide technical support for acidizing operation parameter optimization and acid type selection.
Limestone rock samples were collected from Mishrif Formation of Ahdeb oilfield in Iraq. Mineral composition, porosity, equivalent liquid permeability and acoustic velocity of preliminary core samples were determined. Then, transverse relaxation time (
The experimental results demonstrated that NMR porosity showed good correlation with bulk density, P-wave velocity and S-wave velocity, but little relevance to equivalent liquid permeability. Both of homogeneous pore distribution and low-reaction-rate acid system promoted the formation of multiple ramified wormholes, the same as the role of high acid injection rate. The optimal acid injection rate for 15 wt% hydrochloric acid was 2 cm3/min. Acid volume to breakthrough decreased and increment ratio of porosity increased, as acid injection rate decreased from 3 cm3/min to 1 cm3/min. In comparison, optimal acid injection rate for acetic acid was smaller than that for hydrochloric acid at same acid concentration. Meanwhile, acid volume to breakthrough for acetic acid was larger than that for hydrochloric acid at same injection rate.
The analyzing method proposed in this paper combined the global pore size variation with local wormhole propagation pattern, reflecting the acid flow characteristics under acid-rock reaction, optimum acidizing parameters and acid type when taking both minimum acid volume to breakthrough and increment ratio of initial porosity into consideration. The method and corresponding results were of practical significance to help engineers make rational acidizing design for carbonate and sandstone reservoirs, or acid pretreatment design for high-fracturing-pressure reservoirs such as shale.
To predict liquid-loading tendencies and to identify opportunities for production enhancement, the performance of 150 gas wells was analyzed in two gas fields in India. This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability. The complete paper describes the development of a smart robotic inspection system for noncontact condition monitoring and fault detection in buried pipelines. Multistage horizontal well designs were first implemented in the Bakken in 2007. Since then, more than 12,000 wells have been completed in either the Middle Bakken or Three Forks zones.
Cossa, Alessandro (Eni S.p.A) | Guglielmelli, Andrea (Eni S.p.A) | Rotelli, Fabiana (Eni S.p.A) | Bazzana, Michele (Eni S.p.A) | Callegaro, Chiara (Eni S.p.A) | Raimondi Cominesi, Nicola (Eni S.p.A) | Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD)
Carbonate reservoirs are often characterized by karst features occurrence, usually related to a significant permeability enhancement in presence of low porosity and low permeability matrix type sediments. The distribution of such karst features is generally highly heterogeneous and difficult to predict, making the reservoir management challenging.
In Zubair Field (Iraq), there are numerous evidences of karst events within the Upper interval of Mishrif Formation. The production behavior of Upper Mishrif is therefore very heterogeneous, moving from wells with relatively low flow capacity, as expected from petrophysical interpretation, to wells with a very high flow capacity, hence related to karst enhanced permeability. The integration of petrophysical interpretation, well test and multi-rate production logging allowed to preliminary highlight the improved permeability intervals associated to karst. In addition, accurate image log analysis on the same wells investigated a possible relationship between vug densities and production data, to be extended also to wells lacking the latter data. This process allowed to define a karst flag in more than 60 wells.
Then, correlations between karst features and different seismic and geological attributes were identified. The most meaningful parameters were used as input data for a Neural Net Process, leading to the definition of a probability 3D Volume of karst occurrence.
The final outcomes of the workflow are karst probability maps, used as a driver for the definition of new wells targets and associated trajectories. The recent drilled wells, with optimized paths according to these prediction-maps, have demonstrated the reliability of this approach intercepting the desired karst intervals. This study represents a valuable opportunity in terms of understanding of the reservoir behavior and impact on the ongoing intensive drilling campaign and related field performance.
Copyright 2020, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Dhahran, Saudi Arabia, 13 - 15 January 2020. This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented.