The reservoir quality prediction is carried out at the exploration scale through software that try to model the diagenetic evolution of the reservoir. The input data are quantitative petrographic data, core analysis results and the burial and thermal history of the wells either 1D or 3D (PSM). The procedure starts from the calibration of the model on a well with cores or sidewall cores for petrographic-diagenetic data and RCA and with a calibrated burial and thermal history. Once calibrated, the model extends to the whole area of interest following the 3D burial and thermal history model of the reservoir. The extension of the approach to the reservoir scale requires a dedicated workflow that comprises the following points: - Identification of the main diagenetic issues from petrographic studies; - Use of the information coming from logs of the non-cored wells in a diagenetic perspective; this step comprehends the realization of detailed CPI of wells and, if necessary, additional mineralogical analyses in order to fix a valuable mineralogical model; - 3D burial and thermal history reconstruction at the reservoir model scale using the reservoir model surfaces; the step implies the reconciliation of the regional explorative model with the layers and cell dimension of the reservoir one; - Modelling of specific diagenetic phenomena through transport-reaction models, in order to assess the areal distribution of diagenetic drivers in the reservoir to be used as trends; as an example, carbonate cementation though faults is one of the issues; in this step, also the structural evolution of the reservoir is a key point; - Reservoir quality prediction maps of the reservoir layers; - Use of the maps as soft drivers in the reservoir models and results comparison with other model scenarios (e.g.
A statistical screening methodology is presented to address uncertainty related to main geological assumptions in green field modeling. The goals are the identification of the entire range of uncertainty on production, learning which are the most impacting geological uncertain inputs and understanding the relationships between geological scenarios and classes of dynamic behavior.
The paper presents the methodology and an example application to a green field case study. The method is applied on an ensemble of reservoir models created by combining geological parameters across their range of uncertainty. The ensemble of models is then simulated with a selected development strategy and dynamic responses are grouped in classes of outcome through clustering algorithms. Ensemble responses are visualized on a multidimensional stacking plot, as a function of the geological input, and the most influential parameters are identified by axes sorting on the plot. Geological scenarios are then classified on dynamic responses through classification tree algorithms. Finally, a representative set of models is selected from the geological scenarios.
The example study application shows a final oil recovery uncertainty range of 4:1, which is reasonable for a green field in lack of data. Such high range of uncertainty could hardly be found by common risk assessment based on fixed geological assumptions, which often tend to underestimate uncertainty on forecasts. Ensemble outcomes are grouped in four classes by oil recovery, plateau strength, produced water, and breakthrough time. The adoption of such clustering features gives a broad understanding of the reservoir dynamic response. The most influential geological inputs among the examined structural and sedimentological parameters in the example application result to be the fault orientation and channel fraction. This screening result highlights the main drivers of geological uncertainty and is useful for the following scenario classification phase. Classification of the geological scenarios leads to five classes of geological parameter sets, each linked to a main class of dynamic behavior, and finally to five representative models. These five models constitute an effective sampling of the geological uncertainty space which also captures the different types of dynamic response.
This paper will contribute to widen the engineering experience on the use of machine learning for risk analysis by presenting an application on a real field case study to explore the relationship between geological uncertainty and reservoir dynamic behavior.
Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
A robust analysis of a polymer flooding inter-well pilot has been performed. The main objective was to analyze the polymer injection performance and to confirm the in-situ designed viscosity of the polymer injected, necessary to estimate the expected EOR effect.
The adopted workflow focused on the integration of different sources of data and analyses. Indeed, several phenomena may occur during polymer injection, such as complex injectivity behavior due to polymer non-Newtonian rheological nature, formation damage caused by particles adsorption, fractures opening, and mechanical degradation of polymer. The injection performance analysis focused the following aspects: previous case history, polymer laboratory tests, shear stress through perforation evaluation, diagnostic plots, injectivity test interpretation, well test analysis, and fracturing investigation. Lastly, numerical simulation allowed us to integrate the different analysis, thoroughly capturing the subsurface polymer behavior. Main result is that injection under fracturing conditions occurred during pilot start-up. These small-scale fractures, localized in the near-wellbore zone, are not detrimental to polymer flooding and rather increase the well injectivity. Furthermore, no evidence of mechanical polymer degradation was detected.
The evaluation of the subsurface polymer behavior during an inter-well pilot is crucial to verify the correct polymer injection process. In this work different source of data and analysis have been integrated to understand the injectivity behavior. Robust reservoir monitoring is ongoing and preliminary promising effects are now being shown.
Arata, F. (Eni S.p.A) | Gangemi, G. (Eni S.p.A) | Mele, M. (Eni S.p.A) | Tagliamonte, R. L. (Eni S.p.A) | Tarchiani, C. (Eni S.p.A) | Chinellato, F. (Schlumberger) | Denichou, J. M. (Schlumberger) | Maggs, D. (Schlumberger)
This paper presents a method for integrating information obtained from ultradeep azimuthal electromagnetic (EM) technology, and processed during geosteering activity, to update a 3D reservoir model.
The latest developments in logging-while-drilling (LWD) technology, unimaginable until a few years ago, dramatically improve understanding reservoir structure far away from the wellbore. Ultradeep azimuthal EM technology provided a step change in remote detection capabilities by mapping resistivity contrasts up to tens of meters away from the wellbore. This innovation helps identify unexpected pay zones while drilling, improves subsurface understanding, and leads to well placement optimization in real time. In addition, the multiboundary reservoir mapping, provided by inversion of the ultradeep azimuthal EM measurements, allows for improvement in 3D reservoir model updates when addressing field development optimization.
The method presented integrates field geological knowledge, wellbore-centric LWD data (logs and images), EM reservoir mapping information, and interpreted seismic data to refine a 3D reservoir model in the neighborhood of the well. The ultimate goal is to include the data acquired in horizontal wells in a live reservoir model update across the entire cycle of the well placement workflow. The process includes a feasibility study for technology and strategy selection, real-time geosteering execution and data integration to update the 3D reservoir model in near real time. Collaborative cross-disciplinary teams, composed of both operator and service company specialists, are focusing more and more of their attention on integrating this information into optimal field development strategy.
Nowadays, it is possible for operators to handle multiboundary reservoir mapping data directly within dedicated geological modeling platforms. Advanced software solutions, designed to improve data accessibility, are the base for new integrated workflows for accurate 3D reservoir models using a multiscale dataset.
This paper looks at the Eni energy efficiency program (EEP) for operational and design optimization of the oil ' gas plants. The Oil ' Gas sector is a significant energy consumer and CO2 emitter and the potential for energy saving can allow this sector to play a major role in achieving the Climate Change policy objectives. This paper reports on the work undertaken internally by the Company and focuses on the methodology adopted, the main results achieved in the last years and the potential Energy Efficiency (EE) improvements in future years. Maximization of energy efficiency plant can be achieved by adopting on a regular basis the most important energy efficiency principles and best practices for operation and engineering design.
Applications of onshore pipelines in challenging areas need that the pipeline design, in addition to the traditional stress-based criteria, should also account for the strain which the line may occasionally be subjected to and which could be resp onsible of the final failure. A new plasticity model has been developed by CSM (Iob et Al, 2015) aimed at improving the prediction capabilit y of FE codes, especially for simulating the behavior of high strength steel anisotropic materials. In this paper, the model has been calibrated, implemented into a commercial finite element code and then validated through experimental full-scale tests involving large diameter linepipes. Pipe hydraulic burst and bending tests have been reproduced by finite element analysis up to final failure.
Carbon Capture & Storage ( CCS ) technology was developed to reduce anthropogenic emission of carbon dioxide ( CO2 ) into atmosphere. As the energy sector accounts for around two-thirds of Green House Gases emissions ( GHG ), coming from fossil fuels utilization; the first logical application for CCS was in power generation; for many decades to come, forecasts of global energy demand indicate this fossil fuel dependence will continue. However in the last years, also in other industrial sectors the CCS technology is growing as an option to get emissions cuts at low cost without revamping production technologies.
The estimated growth of the CO2 transportation network, to meet the climate challenge with CCS, is very large compared to the actual carbon dioxide pipelines network having an overall length ( around 6000 km ). Recently, some new stimuli have came up as for application of CCS in refineries, cement plants, gasification uses, cement and iron industries, or fertilizers, chemical feedstock. Furthermore offshore pipeline could enhance the industrial deployment of CCS by avoiding the NIMBY effect of onshore storage sites selection.