Mineral scales frequently form during oil production as the result of changes in temperature, pressure, or the mixing of incompatible formation and injection waters. For hydraulic fracturing, there has been an ongoing effort to replace the use of fresh water with seawater or produced water to address the fresh water shortage issue in many areas in the world. When seawater is injected into the formation, the scaling tendency is inevitable. Among many different types of scale, barite scale, which is a product of the encounter of sulfate ions (typically abundant in seawater) and barium ions (exist at high concentrations in various formations), poses a serious problem. To effectively mitigate this serious scale issue, a combination of water treatment and chemical scale inhibitor is recommended. Nanofiltration (NF) technology has proven to be a reliable water treatment method that specifically removes sulfate ions from water sources with high sulfate content. This paper presents the results of a NF water-treatment process and discusses how the treatment process is a feasible method for barite scale inhibition. In addition, a comparative study of different organic polymers as scale inhibitors was also conducted. For barite scale inhibition in particular, sulfonate polymers were more effective than phosphonate polymers. Scale inhibitor development was conducted using standard evaluation methods, including static bottle testing and a dynamic scale loop. A new, quick laboratory method using ultrasonic vibration was also developed to evaluate scale inhibitor performance during the bottle testing. A combination of NF technology and the new scale inhibitor enabled a new technology to help prevent scale formation during fracturing with seawater or heavy brine water.
Cao, Cheng (China University of Petroleum at Beijing and Research Institute of Shanxi Yanchang Petroleum Group Co. Ltd) | Li, Tiantai (China University of Petroleum at Beijing) | Zhao, Yongpan (Research Institute of Shanxi Yanchang Petroleum Group Co. Ltd) | Xue, Jinquan (Technology Research Center of Exploration and Development of YanChang Oil Field) | Ren, Zhongxin (Gas Storage Project Department, West-East Pipeline Company, PetroChina) | Li, Yudan (China University of Petroleum at Beijing)
In order to characterize the influence of diffusion effect, stress sensitivity, matrix shrinkage and adsorption layer on shale gas permeability, weighted coefficient is used to establishdiffusion permeability model by couple transition diffusion and Knudsen diffusion. And the effect of matrix shrink, stress sensitivity and adsorption layer are considered into shale gas permeability model. The shale gas permeability model is verified by permeability experiment result of shale core in Ordos basin and discussing the effect of each parameter. The results showed that: (1) When the weighted coefficient- Km, n and m is 7, 4 and 16 respectively, the proposed diffusion permeability highly agrees with Fick diffusion, transient diffusion and Knudsen diffusion, that can represent diffusion of the whole flow stage.(2) Under the conditions of low pressure and small pores, diffusion flow is the main control factors of shale gas transmission; and under the condition of high pressure and large pores, viscous flow mainly controlling shale gas transmission.(3) The influence of stress sensitivity and adsorption layer on shale gas permeability is bigger, and the influence of the matrix shrink can be ignored.(4) Adsorption layer have a significant impact on the shale gas permeability under the conditions of high pressure and small pores. The proposed shale gas permeability model can eliminate the deviation result from describing the shale gas diffusion only by using Knudsen diffusion model, and also can describe the shale gas transmission under a multi-physic field, and thus it is significantly enlighten the development of shale gas.
Levannier, A. (Schlumberger) | Chabbert, A. (Schlumberger) | Neumaier, M. (Schlumberger) | Benabbou, A. (Schlumberger) | Viard, T. (Schlumberger) | Macé, L. (Schlumberger) | Santoshini, S. (Schlumberger) | Lepage, F. (Schlumberger) | Malvesin, E. (Schlumberger) | Laverne, T. (Schlumberger) | Daly, C. (Schlumberger) | Buchholz, C. (Schlumberger) | Wang, Y. (Schlumberger)
Historically it has been a challenge to rapidly produce a geomodel that can honor the detailed form of complex faulting and folding, while enabling sensible property modeling and that is tailored to fluid flow simulations. In structurally complex areas, the construction of accurate 3D geological models is often impeded by the complexity of the fault framework, the resulting layer segmentation, "multi-z" horizons in compressive settings and steeply dipping to overturned layers. In particular, standard geocellular models, such as pillar grids, may fail to honor complex structural features.
To address those issues, methodologies using a mapping between the geological space and a 3D parametric space — often referred to as depositional space — have been described in the literature for geological grid construction and property population. Using case examples of structurally complex settings, we illustrate a depositional unstructured grid construction workflow. Compared to known methodologies, the depositional space is computed using a geomechanically-based approach. We illustrate that the methodology allows for complex structural configurations to be effectively modeled and transformed into a geocellular model honoring the full structural complexity. Our depositional unstructured model can then be populated with properties and used directly for flow simulations.
Material compatibility is key to proper equipment design, operation and reliability in both well and artificial lift completion. This paper addresses material compatibility lessons learned from well completion components exposed to harsh hydrocarbon and saline subsurface environments.
Dismantle Inspection Failure Analysis (DIFA) was utilized to ascertain the failure root cause for 25 water source wells utilizing Electrical Submersible Pumps (ESPs). Positive Material Identification (PMI) testing was used to identify the cause of 17 of the failed completions — incompatible material selection resulting in completion workover after an average of 562 days of production. Moreover, X-ray Power Diffraction (XRD) and Energy Dispersed Spectroscopy (EDS) analysis were used to characterize deposit samples from the pulled equipment.
It was discovered, upon pulling of the failed completions, the tubular pup joint above the ESP pump discharge head contained holes due to corrosion. From the various analysis and tests, it was determined the pup joint (a 7″ tubular) was made of carbon steel while the ESP was made of super duplex steel. Laboratory analysis further proved these two metal materials were not compatible in the harsh high chloride environment, which resulted in galvanized corrosion of the pup-joint above the pump discharge head. Galvanic corrosion is an electrochemical process in which one metal corrodes preferentially to another when both metals are in electrical contact in the presence of an electrolyte. A geochemical analysis of the water from these wells indicated a high concentration of aggressive species including elevated Total Dissolved Solids (TDS), chloride, sulphate and carbonate. The composition is considered highly conductive and highly corrosive to bare carbon steel. Energizing of the ESP power cable provided the electro-magnetic field that aided migration of electrons across the carbon steel of the tubular pup joint and the duplex stainless steel of the pump discharge head. A guideline for selecting two dissimilar metals based on the volts differential across the metals and higher grades of tubular materials were recommended for such a harsh environment.
A guideline for selecting dissimilar metals for compatibility — based on the lessons learned from the ESP completions and the recommendations made to improve runlife of ESP completions in harsh high-chloride environment — is presented in this paper. The recommended approach was applied in recompleting the failed and pulled 25 ESP completions utilizing L80 modified 13-chrome and Glass Reinforced Epoxy (GRE) lined tubular according to the guideline work detailed in the paper.
Though there are various methods to assess reservoir performance, historical methods seem to focus the assessment on a single or couple of parameters. These include traditional methods to evaluate the reservoir sweep through average oil saturation-thickness maps, remaining oil volume maps, etc. Optimum reservoir management is a challenging and time consuming process since it usually involves analyzing many reservoir properties such as porosity, permeability, thickness, hydrocarbon saturation, fluid properties, relative permeability, net-to-gross ratio and pressures. In this work, we incorporate all these parameters into an automated workflow for reservoir diagnostics; and identification and ranking of optimum hydrocarbon (HC) targets.
The proposed workflow extracts static and dynamic information from reservoir simulation outputs and performs additional post-processing calculations on each grid cell for all time steps. The methodology involves classification of the reservoir simulation grid cells based on fluid saturation, relative permeability, pressure changes and displacing phase fluxes. After that, Produced, Mobile and Immobile oil volumes are calculated for each cell. These volumes are then grouped into six categories, namely, Produced, Highly Contacted, Moderately Contacted, Minimally Contacted, Uncontacted and Immobile Oil. In addition, the workflow incorporates different indicators for determining grid cell quality. These indicators are Reservoir Opportunity Index (ROI) and Simulation Opportunity Index (SOI); and we proposed a new reservoir quality indicator that incorporate changes in pressure over time. Finally, the workflow identifies connected cells with high quality indices and ranks these regions based on size and/or grid cell quality as potential targets for infill drilling.
The presented automated workflow is introduced as an integral part of well placement optimization workflow. It has been tested on several simulation models and successfully identified and ranked un-swept reservoir regions which proved through dynamic simulations to be credible future drilling targets.
Reddy, S. S. (Oil and Natural Gas Ltd) | Anjaneyulu, J. V. (Oil and Natural Gas Ltd) | Lal, Abhay Kumar (Oil and Natural Gas Ltd) | Rao, E. J. (Oil and Natural Gas Ltd) | C H, Ramakrishna (Oil and Natural Gas Ltd) | Talreja, Rahul (Schlumberger) | Bahuguna, Somesh (Schlumberger) | Zacharia, Joseph (Schlumberger) | Chatterjee, Chandreyi (Schlumberger) | Basu, Jayanta (Schlumberger)
Malleswaram field in Krishna-Godavari (KG) basin has proven gas reserves in the late Cretaceous Nandigama formation. Many drilling challenges were faced, including losses, tight hole, and stuck pipe in the Raghavapuram and Nandigama formations overlying the reservoir interval. This study was conducted to provide a solution for drilling optimization by mitigating drilling-related nonproductive time (NPT). Integration of acoustic and geochemical data for geomechanics study provided a new insight into cause of overpressure and need for revamping of casing policy to significantly improve wellbore stability, mitigate risks, and ensure future drilling success. Generated stress models can be used to optimize hydraulic fracturing in these reservoirs. A completion quality based on stress model indicates the need for multistage fracturing due to the presence of stress barriers inside sand units in Nandigama formation.
Identifying potential productive reservoir units for infill drilling is a major challenge in developing giant fields in order to meet production targets and extend plateau. A common practice in identifying potential drilling locations is using oil saturation from production logs or near-by wells performance. Literature already recommended combining static and dynamic parameters from reservoir models (geological or numerical) to calculate cell performance indices such as Reservoir Opportunity Index (ROI) or Simulation Opportunity Index (SOI). It is difficult for those methodologies to provide volumetric representation of the hydrocarbons in potential areas since they do not specify the details of the clustering mechanism of similarly performing cells. The proposed algorithm allows the reservoir engineer to, progressively and recursively, define hydrocarbon sweet spots areas.
In this work, progressive-recursive self-organizing maps (PR-SOM) is developed and tested on a carbonate reservoir model. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the simulation grid cells into potential drilling targets by using a progressive list of dynamic or static reservoir parameters to identify similarly "good" contiguous regions. This is achieved by applying SOM on geomodels based on relative permeability, fluid saturation, pressure, and displacing fluid influx in the first iteration. The second iteration, PR-SOM explores the already selected regions and applies SOM on mobile and immobile hydrocarbons. The last iteration recursively applies PR-SOM to identify areas away from existing wells on the already defined regions from last iteration. The algorithm allows further definition of sweet spots based on more parameters and will further increase the potential value of the classified regions.
PR-SOM was applied to a carbonate reservoir with the objective of identifying un-swept areas as potential candidates for infill drilling. To compare the resulting potential target regions, an implementation of sweet spot identification algorithm and conventional approaches were applied on the same field. The results show that PR-SOM generated more accurate and conservative regions reducing the risks and increasing the confidence in the designated regions. In addition to obtaining more accurate clustering results, using PR-SOM allows extending the search to increase the value of required targets whereas previous work has to adhere to the original selected parameters (static or dynamic) for region identification and selection.
An ensemble-based 4D seismic history matching case is presented. Seismic data are re-parameterized as distance to 4D anomaly front and assimilated with production data. The field is a large turbiditic system, with initial fluid pressure close to the bubble point. Production causes the pressure to fall below the bubble point, resulting in a widespread gas-exsolution. The time-lapse change in gas saturation is considered responsible for the observed negative relative changes in seismic velocity seen over the all reservoir. This study is innovative for two reasons. First, the distance-to-front parameterization is applied to the gas-phase which appears everywhere in the field, rather than coming form an injection source like in previous application of the parameterization. Second, the binarization of the simulated time-lapse anomaly is performed circumventing the use of a petroelastic model; the petroelastic model would be necessary to relate the measurements to fluid properties changes and to decide a threshold for binarizing observations and pressure. However, the effect of gas is so widespread and evident that the petroelastic model is replaced by a clustering approach based on the gas saturation change of the reservoir cells. This study shows that adding the 4D re-parameterized seismic data in addition to the production data is keeping a reasonable match with production data while constraining the overall gas distribution in the reservoir to the observed seismic data.
After great success of 4D seismic (Time-lapse) in clastic environment lot of 4D projects started 10 years ago in carbonates. Because of a smaller petroelastic response to reservoir modification lot of reservoirs enginners and geophysicists considered that 4D in carbonate has a very little probability of success.
To check the capacity of 4D seismic to work in carbonate environment ADMA decided to acquire a 4D pilot on one of their giant field. The design of this pilot has been chosen to mimic as perfectly as possible the base survey. Then another acquisition has been performed with a fully different design that reduces drastically the cost of survey. TOTAL had the great opportunity to process, invert and interpret these datasets. This paper will show some comparison of 4D results obtained from the 2 designs.
TOTAL performed a lot of QCs allowing to get a very good appreciation of degradation in repeatability and increase of noise levels linked to acquisition design. The regular QCs as NRMS or predictability are compared but also some new in house TOTAL QCs as SDR (Signal Distortion Ratio), Noise Xplots, etc… Next relevant 4D attributes (like relative changes in P velocities and in P impedances) are computed using in house algorithms that have been developed on numerous Total portfolio cases. They can be compared for both designs using as reference the pilot which is considered closer to reality.
A first step was to validate 4D anomalies out of pilot area by comparing them to geological features and production knowledge. So we got the proof of real 4D anomalies related to clear production phenomena, validated by the asset team.
Second step was to evaluate the stability of 4D results despite the acquisition design modification and the strong variation of noise level. It is obvious and expected that the "non repeated" design introduces a degradation of the quality of 4D results. Key points are on one hand to check if the 4D processing can handle these differences successfully and another hand to define recommendation to acquire a successful and cost-effective monitoring survey.
In conclusion this example shows that 4D seismic in carbonates, oil/water system; with difficult seismic environment is definitely possible under condition that acquisition, processing and inversion of 4D seismic are conducted with care to avoid the weakest link limits value of 4D information
The Jurassic mud rocks of Jafurah Basin are one of the most promising shale gas reservoirs in Saudi Arabia, retaining considerably high total organic content (TOC) values, and being the source rock for the world-class oil fields of the Kingdom. The purpose of this study is to build a calibrated model with core data using an integrated formation evaluation approach. The model then is frequently used to estimate reservoir properties, minimize uncertainty, and influence decisions on better well placements.
The Tuwaiq Mountain shale play is mainly composed of mudstone with few fraction of dispersed detrital minerals. The Tuwaiq Mountain Formation is divided into two parts: Upper and Lower, where the Lower Tuwaiq Mountain contains higher organic matter and so better reservoir quality as compared to the Upper Tuwaiq Mountain.
The formation evaluation of unconventional shale gas reservoirs presents numerous challenges. The conventional porosity logs, density neutron, and sonic, are heavily affected by the presence of organic matter. The estimation of initial hydrocarbon in place requires accurate estimation of formation water saturation. The conventional equations used to estimate the formation water saturation are subject to a high degree of uncertainty, mainly related to a complex wettability system and unknown formation water resistivity. Therefore, these challenges require the use of advanced and calibrated well logs. The advanced well log technologies used in this study are pulsed neutron elemental spectroscopy and nuclear magnetic resonance (NMR). The analysis can only be achieved through comparison and calibration with micro and nanoscale core data to help building an accurate petrophysical model. The use of the pulsed neutron elemental spectroscopy tool allows the estimation of rock composition, the evaluation of the amount of total carbon present in the system, and consequently, the amount of organic matter in the formation.
Natural magnetic resonance tools are lithology independent and provide an accurate estimation of total porosity. In unconventional shale gas intervals, the T2 distribution is mainly controlled by the surface relaxation factor, and so can be directly linked to the pore size distribution. By applying an appropriate cutoff, based on SEM results, continuous estimation of organic and inorganic porosity can be directly derived from NMR T2 distribution. A saturation model can also be built from a function, linking formation water saturation and organic carbon porosity. The core analysis data are used to understand the pore structure and calibrate the well logs.
The study has proven that the NMR technology works effectively in determining the total porosity, pore system distribution, and estimates of formation water saturation. The nanoscale core analysis is then used to understand the pore structure and to calibrate the well logs.