SPE Latin America and Caribbean Petroleum Engineering Conference

Operational execution of Fluid Sampling technologies in the logging-while-drilling (LWD) environment compared with Wireline requires a different set up and allows new operational capabilities for LWD. The objective of this paper is to identify what are the jobs operational risks, in order to select the best LWD technologies and operational approach to identify and mitigate these risks while drilling, resulting in the fastest and cleanest reservoir sample. LWD fluid sampling technology brings three new operational capabilities to this type of service: ability to select pad orientation; drilling fluid flow is required to keep the BHA energized and real time (RT) data telemetry and; capability of operating in HAHZ wellbores without additional risk. To take full advantage of these new capabilities, there must be a full understanding of the relationship between wellbore and formation, analyzing subjects such as filtrate invasion profile, borehole stability, sand production, petrophysics and LWD FE. The ability to choose pad direction, coupled with high end technologies, such as NMR and resistivity images generate important capabilities to be evaluated considering formation quality and borehole condition, allowing the selection, not only of the best depth to sample, considering petrophysical properties, but also the optimum pad direction, considering borehole conditions. Images allow the identification of drilling induced fractures, breakout, faults and thin bed, making it possible for RT interpretation for optimum pad direction, avoiding undesired features. Prior geomechanics study help identify issues that might come up during fluid sampling operation, such as breakout, sand production and borehole failure related to bedding plane. Technologies such as acoustic, NMR and images allow RT evaluation of these issues and the ability to select pad orientation and nonstop drilling fluid flowing may result in correcting these issues. Filtrate invasion profile generates complex geometries with lateral displacements and gravitational segregations. Prior study of invasion profile reservoir and drilling fluid properties, thin bed analysis and reservoir/non-reservoir interface analysis must be considered to achieve optimum operational time. This paper presents a technical and operational approach for LWD fluid sampling operations, regarding FE, geomechanics and fluid invasion profiles, which minimizes operational risk and optimizes sampling time.

The in-line scavenging of hydrogen sulfide is the preferred method for minimizing the corrosion and operational risks in oil production (Rajagopal et al. 2009). On the other hand, it is known that Nanoparticles by their small size and high dispersion increase the probability of collision between molecules and minimize the diffusion problems. Additionally, oxides of iron, manganese, zinc, magnesium and copper are considered suitable sorbents for gas desulfurization at temperatures in excess of 300°C (Husein et al. 2010). The experiments were designed to apply nanoparticles for the streams treatment during enhanced heavy oil recovery (steam injection). Sorption of the H₂S present in oil phases finds application for mitigating H₂S forming during this process. In this study, our heavy crude oil whose viscosity is 10500 mPa.s, was obtained from a reservoir block of the Faja Petrolífera del Orinoco Hugo Chávez (FPOHC, Venezuela), with temperature about 80°C, and when the steam is injected temperature reach up to 260°C. Three types of nanoparticles were used to treat the heavy crude oil (H₂S scavengers and reduce viscosity). The organic matrix was high vacuum gas oil (HVGO) which serves as the carrier of dispersed particles, to reduce both H₂S production and viscosity, and the nanoparticles, based on transition metals that serve as H₂S scavengers centers are, respectively, FexOy, CuxOy and NixOy. In these experiments, before reaction, the sand and the gaseous products were separated to remove any catalytic effect from the minerals and H₂S present. Experiments were carried out in an autoclave reactor simulating steam injection condition (~600 psi and ~260°C). Appropriate quantities of heavy crude oil and water, and 500 ppm of dispersed nanoparticles were placed in the reactor; methane was used as reservoir gas. Results indicate the reduction of H₂S production from 1500 ppm in the experiment without nanoparticles up to 100 ppm, 250 ppm and traces to FexOy, Ni_xO_y and Cu_xO_y, respectively. Also, reductions of viscosity were obtained for all tests, from 15 to 26%.

Fast simulation algorithms based on reduced-order modeling have been developed in order to facilitate large-scale and complex computationally intensive reservoir simulation and optimization. Methods like proper orthogonal decomposition (POD) and Dynamic Mode Decomposition (DMD) have been successfully used to efficiently capture and predict the behavior of reservoir fluid flow. Non-intrusive techniques (e.g., DMD), are especially attractive as it is a data-driven approach that do not require code modifications (equation free). In this paper, we will further enhance the application of the DMD, by investigating sparse approximations of the snapshots. This is particularly useful when there is a limited number of sparse measurements as in the case of reservoir simulation.

The approach taken here is the snapshot-based model reduction, whereby one computes a sequence of reservoir simulation solutions (e.g., pressures and water saturations in the case of two-phase flow model) forming a big data matrix – we call this the offline step - that is used to compute basis for representing the states of the system for different input parameters – the online step. The selection of these few basis is the core of the model reduction methods. DMD selects the basis and apply the reduction without knowledge of the inner works of the reservoir simulator, as opposed to the POD methods. Sparse DMD has been introduced recently to determine the subset of the DMD models that has the most profound influence on the quality of the approximation of the snapshot sequence. Two model reduction process are involved. One is offline process, which does not require running the simulator but rather predicting future behavior with linear combination of DMD modes. The other online process incorporates sparsity DMD modes in numerical simulator to release the burden of linear matrix solver.

We first show the methodology applied to a 3-D single phase flow problem. Here we show the DMD modes and its physical interpretations, and then move to two phase flow for 2-D heterogeneous reservoir using the SPE-10 benchmark. Both online and offline process will be used for evaluation. We observe that with a few DMD modes we can capture the behavior of the reservoir models. Sparse DMD leads to the optimal selection of the few DMD modes. We also assess the trade-offs between problem size and computational time for each reservoir model. The novelty of our method is the application of sparse DMD, which is a data-driven technique and the ability to select few optimal basis for the case of reservoir simulation.

Tight gas reservoir has potential to provide a significant contribution to meet the global energy demand. Unconventional resource plays and in particular tight gas reservoir, are generally characterized by lower geologic risk but higher commercial risk. For that reason, a precise understanding of the potential range can lead to the commercial success; this weighs on the economic evaluation process.

The cutting-edge method "Technical Datamining" (DM), use artificial intelligence, statists, and algorithm of learning machines to accomplish new knowledge of clustering and predictive types. Neural networks-DM are computational models that have been used in different research fields with outstanding results. Thus, models of temporal series are pursued to develop to achieve reliable estimations of the main economic indexes: NPV, IRR, Payout and investment performance in the high risk Oil & Gas portfolios, in particular economic evaluation of unconventional/Tight Gas resources, which is our concern. Neural networks learn from experience and errors: when more wells of the investments portfolios are added, the experience will improve.

The process of knowledge improvement begins with the extraction, transformation and loading data to the collection of the resultant model and its analysis. This involves an exhaustive work with the exploration and evaluation with the behavior of independent variables (Capex, Opex, Reserves, Gas Price and Time), the outliers, the normalization, variability and the distributions. Furthermore, it is vital to maintain a complex and extensive training of the neural network model with different parameters and iterations, using the previous experience´s expert. Our study has 4 years and a monthly seasonality for processing the data in the search to optimize decision making.

The model application will be developed in the sectoral block of the Lajas Formation of the Neuquen Basin, with six wells in production, the GOIS value above 3000 MMm3 and the current recover factor estimated in 19 %. In addition to this, are expected the incorporation of new wells to the block to increase the recovery factor above 35 % and thus improve the return on investment (NPV / Investment). Finally, the construction of neural network model will provide predictive values more precisely through a time series using 80 % focusing on tasks for training and 20% for testing, with minor errors of 5 %. ▪

Extracting hidden knowledge or information not trivial of dataset to be used in making decision.

Over last 10 years, Bolivia has gone through a process of significant transformation of its legal system: starting with a new Political Constitution of the State and continuing with new Laws, Supreme Decrees and Regulations that have affected society as a whole and some had specific and direct impact on the new discoveries due to the process of consultation and compensation of Indigenous People when their lands are affected by oil and gas field developments. This paper intent to demonstrate how those norms don't provide the economic balance that could render the norm to be considered as efficient. This paper does not analyse specific initiatives taken by private companies in accordance with their internal policies regarding sustainable development of local indigenous communities.

This paper examines the maturity degree and potential of the Golfo San Jorge basin which represents 50% of Argentina's daily oil production and 2/3 of its oil reserves. It has approximately 40,000 wells drilled to date and its daily oil, water and gas production are 39, 505 Mm3/d and 15 MMm3/d respectively with 509 Mm3/d of water injection (as of December 2016). The GSJ basin was discovered in 1907, being the first developed oil basin in Argentina. A thorough revision of production and injection history, well status, drilling history and reserves data for 160 fields, combined with lessons learned from more than 15 years of experience, 5000 wells and several detailed waterflooding (WF) studies, allowed to classify different maturity degrees in order to preliminary assess the currently reported reserves and detect opportunities to increase recoveries. The opportunities detected were grouped in two main categories: i. Optimization of ongoing WF processes: these are low risk / low investments activities aiming to improve contacted area and/or sweep efficiency and ii.

In order to extract the oil from reservoirs which have reached the end of the natural flow, the use of an artificial lift system is necessary. The mechanical pumping system accounts for about 90% of all artificial lift systems in the world. This kind of pump has a highly complex mechanical design and serves as a test for evaluating the developed mathematical model and the construction of special measuring equipments for future analysis systems. This work includes the construction and performance testing of an electronic force sensor installed at the Pitman (rod) and an acceleration sensor installed on the Walking Beam. The design and development of these sensors are discussed from the basic including the process of selection and configuration of strain gauges for the signal conditioning for use in harsh environments with interferences during an necessary continuous data transfer. After the acquisition of data in real time, an analysis of the mechanical pumping system using the developed mathematical model and the measured data is performed. The validation of this model is performed by comparison of force and position (angle) values of the measurement and the simulation. The entire system was implemented in software and hardware, and the results are evaluated in field tests in Gänserndorf, Austria.

The increasing complexity of reservoir responses, especially with unconventional reservoirs, calls for more accurate methods of reservoir characterization and well test interpretation. Though the unconventional gas reservoirs are characterized with low flow rates, these can be high enough to exceed the laminar flow range as described by Forchheimer number given in [1]. Moreover, prolonged transient flow in such unconventional reservoirs makes the use Odehs steady state or pseudo-steady state models ([2] and [3] respectively) inappropriate. This paper investigates the combined effects of turbulence, partial penetration, wellbore storage and reservoir damage in wells, especially during the transient flow period, as an attempt to reduce erroneous estimation of reservoir parameters during well test analysis.

Many models to address the reservoir response of partial penetration wells have evolved with time as seen in the works of [2], [3], [4], [5], [6], [7] and [8]. Most of the models however, do not address the commingled effects of partial penetration with other wellbore or reservoir boundary conditions. For this reason, the combined effects of partial penetration with different wellbore and reservoir conditions are investigated. The model was developed based on the composite reservoir concept to incoporate the skin effects analogue Odeh [3] pseudo-steady state model. Flow in the reservoir is addressed in both the radial and vertical directions and by transforming the pressure diffusivity equations using the Laplace and finite Fourier transforms, a diffusivity equation analogue [6] can be derived. Turbulence flow in the reservoir was addressed using the Forchheimer model with transient turbulent flow incorporated analgue Kome [9] and modified to addressed partial penetration effect of the wellbore flowing conditions by transforming the wellbore conditions using the finite Fourier cosine and sine transforms.

With the model, the influence of each of the reservoir parameters/ wellbore flowing conditions are depicted. By considering turbulent flow during the transient phase of production, the partial penetration skin also becomes influenced by turbulence effects. By incorporation the composite reservoir model for the damage skin, the partial penetration effects does not only limit to the vertical and radial permeabilities of the undamaged zone, but also addresses the effects of alterations in vertical permeability in the skin zone. As seen in the work of Kome, 2017, the permeability conventionally derived during IARF for the transient turbulent regime is a function of the Forchheimer number hence approaches will be made as to how to elimate these effects for the partially penetrated well.

The novel model clearly shows the effects of different wellbore flowing conditions and reservoir parameters for a partially penetrated well, especially during the transient flow period and the possible errors made in the past with respect to permeability and damage skin

This paper presents the results of a research conducted to assess the effects a 2D seismic survey may have had on medium and large mammals living in the Lower Urubamba basin (Cuzco, Peru), performed in the frame of the Camisea Project Biodiversity Monitoring Program (PMB). The research was conducted over an area of 900 ha, characterized by dense amazon rain forest and rich biodiversity, representing one of the 35 world biodiversity hotspots identified by Conservation International. Thirty four camera traps were installed along the seismic lines. Results yielded significant information regarding the effects diverse anthropogenic disturbances had on medium and large mammals in the course of the survey, concluding that, although some limited impact in time and space were identified, most of the species recorded remained in the surroundings, with little and only temporary drive away. In addition, the survey allowed the identification of 23 mammal species, many of them indicators of good conservation conditions.

There is a well-known theoretical chart that shows how compression, scales, liquid loading, corrosion, etc. appear as a gas field decreases production due to reservoir depletion. The approach of this paper is ambitious and will demonstrate and exemplify how these problems appeared in our gas field, and share the techniques, methods, and procedures we went through to satisfactorily handle them.

This paper shows the development of a gas field placed in the Golfo San Jorge Basin (Argentina) including the different life stages of the field (High/Medium/Low Pressure) with the related problems in Facilities, Flow Assurance, and Liquid Loading, and finalizes with an introduction to the future problems we are expecting.

Throughout the paper, we will show the changes we went through, lessons learned, and conclusions related to the following topics:

+ Facilities → Slugging in flowlines/changes in suction pressure/new facilities

Not many papers cover in such an integral way the development of a conventional gas field with a large exploitation history as this work does, where the field dates from the 2000s.

This paper sets a reference and fills a gap in terms of an overall look at all problems together, and integrates the theory from the literature, information from field experiences, best practices, and the application of new technologies/methodologies. Challenges for future exploitation are presented as well. Reading this paper will help in understanding the complexity that those people involved in the management of a gas field will face as their reservoirs deplete and the production of the wells goes down.